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CHINA ROOM AIR CONDITIONER & PACKAGE AIR CONDITIONER GROWTH AND ITS IMPACT ON

ELECTRICAL POWER SUPPLY: THE FORECAST PERSPECTIVE

中國小型空調器成長對電力供應的衝擊：商情預測觀點 Jen, You-ching

A thesis submitted in partial fulfillment of the requirements for the degree of

Executive Master of Business Administration

National Cheng Chi University

Approved by: ________________________________Dr. Kuo, Biing-shen Chairperson of Supervisory Committee

Supervisor Committee: ___________________Dr. Dr. Lin, Shin-ju Supervisor Committee: ___________________Dr. Tsay, wen-jen

Program Authorized to Offer Degree ________________________

Date: January 30th, 2007

Abstract

China Room Air Conditioner (RAC) & Package Air Conditioner (PAC) Growth and its Impact On Electrical Power Supply: The Forecast Perspective

Jen, You-ching

Chairperson of the Supervisory Committee:

Dr. Kuo, Biing-shen Department of International Business

National Cheng Chi University

Abstract

Chinese market opened to the world since the establishment of Shenzhen Special Economic Zone (SEC) in 1978. Its economic growth starts taking off from 1992 when the 2nd SEC at Shanghai was established. China’s yearly Gross Domestic Products (GDP) growth exceeds 9% on average since then. The trend continued after China entered World Trade Organization (WTO) in 2001. Their GDP reached Rmb18.232 trillions in 2005. Behind the magnificent growth, is the enormous amount of energy and nature resource consumption, thus lead to the environment damage. In 2003, China announced its plan to build a harmonious society (or well-off society or xiao-kang in mandarin) by 2020. This plan outlined the standard of living for the future Chinese. It will be a society composed mainly by the middle class, with a projected 1.5 billion total population, of which 60% will live in the urban area. GDP will grow to 4 times of what it was at 2000; which will make China the third largest economic in the world. However, can China sustain the growth? Is the supply of energy and resource unlimited? Can the rest of the world afford a developed country with 1.5 billion populations without the shortage of nature resource shortage elsewhere?

This study reviewed China’s residential and light commercial unitary air conditioning market growth trend from 1995 to 2005. This segment represents 85%~90% of the total Chinese market. The study used several business forecasting methods, to develop a model for estimating the room air conditioner (RAC) and package air conditioner (PAC) market growth till 2020, by considering various social and economical factors such as GDP growth, construction of new

buildings, disposal income (DPI) and retail price changes. The forecast can be used to estimate the peak time electrical power demand with the Energy Efficiency Ratio (EER: rated Cooling Capacity in Watt / Power Input in Watt) of RAC and PAC required by the code. Our study found that China should impose a much more stringent EER code in order to reduce peak electrical power demand and avoid supply shortage during the summer, since the economy is predicted to continue to outgrow the power supply. The alternative is to build more new power plant only to meet the summer peak load, while most other time of the year the plants may stay idle.

According to Asia Pacific Economics Cooperation, Energy Standard Information System (APEC ESIS) current China EER requirement is around 25% below Taiwan, and most developed countries. Japan Air Conditioning Journal (JARN) and Building Services Research and Information Association from UK (BSRIA) reported that the size of China’s air conditioning market size exceeded Japan in 2003 to become the world second largest in the world, after only the United States. China now manufactures more mini split than any other places in the world. There should be no technical difficulty to adopt the more stringent code to help reducing the peak load demand from air conditioning, since most advance technologies are now available to the market and local manufacturers. Some Chinese air conditioning manufacturers already have a capacity surplus. Exporting their products to the developed countries such as the US and Japan, will require them to meet the EER code of the country of destination. This may speed up the introduction of higher EER design products in the local Chinese market.

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TABLE OF CONTENTS

Chapter Page Chapter 1 Introduction................................................................................................................1 Chapter 2 Model Building...........................................................................................................4 Chapter 3 Comparison with Other Forecast ..........................................................................32 Chapter 4 The Applications......................................................................................................44 Chapter 5 Conclusion................................................................................................................56

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LIST OF TABLES

Number Page Table 2-1 Indicate 1995~2000 RAC/PAC Market Acutal from BSRIA .......................................5

Table 2-2 Indicate 2001~2005 RAC/PAC Market Actual and 2006 Estimate from BSRIA......6

Table 2-3 Indicate RAC/PAC Market Projection till 2010 from BSRIA.......................................6 Table 2-4 RAC+PAC total in quantity 1995~2005 from BSRIA……………………………..7 Table 2-5 Gross Domestic Products fromChina Statistics Yearbook Chart 3-1……………….8 Table 2-6 Per Capital Gross Domestic Products from China Statistics Yearbook Chart 3-1…..9 Table 2-7 China GDP Adjustment by the Statistics Bureau on Feb. 28, 2006…………………9 Table 2-8 Revised Gross Domestic Products by the Statistics Bureau, Feb. 28, 2006………….9 Table 2-9 Floor Space of Building Under Construction from China Statistics Yearbook Chart 15-11…………………………………………………………………………………10 Table 2-10 Floor Space of Building Completed from China Statistics Yearbook Chart 15-11...10 Table 2-11 Per Capital Annual Disposal Income of Urban Households from China Statistics Year book Chart 10-2……………………………………………………………..11 Table 2-12 Urban Households Consumption Expenditures from China Statistics Yearbook Chart 3-18………………………………………………………………………………….12 Table 2-13-1 General Retail price index from China Statistics Yearbook Chart 9-7………….13 Table 2-13-2 General Retail price index adjusted, using 1995 as 100..............................................13 Table 2-13-3 General Retail price index inverted, using 1995 as 1.0...............................................13 Table 2-14-1 Ex-factory price index of durable consumer goods from China Statistics Yearbook Chart 9-12……………………………………………………………………….14 Table 2-14-2 Ex-factory price index adjusted, using 1995 as 100....................................................14 Table 2-14-3 Ex-factory price index inverted, using 1995 as 1.0……………………………..15 Table 2-15 Urban Population ratio from china Statistics Yearbook Chart 2-3..............................16 Table 2-16 Number of air conditioner owned per 100 urban households at year end from

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China Statistics Yearbook Chart 10-14……………………………………………………...16 Table 2-17-1 Comparing GDP with Per Capital GDP using liner regression analysis with FX17 Table 2-17-2 Comparing Floor Space Completed against under construction, using linear regression analysis with FX…………………………………………………………………18 Table 2-18 Dependent and Independent factors in logarithm……………………………….25 Table 2-19-1 Differencing the logarithm data……………………………………………….28 Table 2-19-2 Converting linear regression results…………………………………………....29 Table 3-1 The results of the forecast, using the best fitting model…………………………...32 Table 3-2 Summary of results using different forecasting method…………………………...34 Table 3-3 Economy indices Target for 2020………………………………………………...40

Table 3-4 Estimated Total Air Conditioner in Operation by 2020...............................................41

Table 4-1-1 China Room Air Conditioner Minimum Energy Performance Standard................45 Table 4-1-2 China Labeling Program……………………………………………………….45

Table 4-2 Taiwan Room Air Conditioner MEPS............................................................................46

Table 4-3 Taiwan’s RAC and PAC Future EER Requirements ....................................................47

Table 4-4 JIS’s Room Air Conditioner MEPS ................................................................................48

Table 4-5 Recent Years Electrical Power Supply Shortages in China...........................................51

Table 4-6 Total Air-con Peak Load Demand Estimation by 2020..................................................53

Table 4-7 Beijing Summer Peak Electrical Load and ratio by Air Conditioner...........................54

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LIST OF CHARTS

Number Page Chart 2-1 China GDP Growth from 1952 to 2005, excerpt from Wikipedia ...............................8 Chart 2-2-1 Retail price index trend analysis, using FX……………………………………...19 Chart 2-2-2 Urban population trend analysis, using FX……………………………………..19 Chart 2-2-3 Number of air conditioner owned trend analysis, using FX…………………….20 Chart 2-3-1 Double-exponential method forecast chart, using FX…………………………..20 Chart 2-3-2 Double-exponential method forecast audit trail, using FX……………………....21 Chart 2-3-3 Double-exponential method forecast smoothing-Holt, using FX……………….21 Chart 2-4-1 Linear Regression method forecast chart, using FX…………………………….21 Chart 2-4-2 Linear Regression method forecast audit trail, using FX………………………..22 Chart 2-4-3 Linear Regression method forecast, using FX…………………………………..22 Chart 2-5-1 Multiple Regression method forecast chart, using FX…………………………..23 Chart 2-5-2 Multiple Regression method forecast audit trail coefficient, using FX…………..23 Chart 2-5-3 Multiple Regression method forecast audit trail statistics, using FX…………….23 Chart 2-5-4 Multiple Regression method forecast, using FX………………………………...23 Chart 2-5-5 Multiple Regression method statistics summary, using FX……………………...24 Chart 2-6-1 Logarithm pro-cast method statistics summary, using FX……………………....25 Chart 2-6-2 Logarithm Linear Regression method statistics summary, using FX…………….26 Chart 2-7-1 Logarithm Multiple Regression method forecast chart, using FX……………….26 Chart 2-7-2 Logarithm Multiple Regression method forecast, using FX……………………..26 Chart 2-7-3 Logarithm Multiple Regression method audit trail coefficient, using FX………..27 Chart 2-7-4 Logarithm Multiple Regression method audit trail statistics, using FX………….27 Chart 2-7-5 Linear Regression of differencing data…………………………………………28 Chart 2-8-1 Logarithm Multiple regression chart w/o retail price and GDP, using FX……...30 Chart 2-8-2 Logarithm Multiple regression forecast w/o retail price and GDP, using FX…...30

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Chart 2-8-3 Logarithm Multiple Regression coefficient w/o retail price and GDP, using FX..30 Chart 2-8-4 Logarithm Multiple Regression statistics w/o retail price and GDP, using FX….31 Chart 3-1 Survival Function of Air Conditioner .............................................................................42

Chart 4-1 Current and Proposed MEPS levels in five countries...................................................49

Chart 4-2 Sales-weighted average EER in five countries ..............................................................50

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ACKNOWLEDGMENTS

The French philosopher and physician Dr. Albert Schweitzer (1875~1965) once said, “In everyone's life, at some time, our inner fire goes out. It is then burst into flame by an encounter with another human being. We should all be thankful for those people who rekindle the inner spirit.” The EMBA program does just that to me, I am grateful for the opportunity and wonderful learning experience. The school life also helped me add a powerful networking.

I would like to thank Chen-chi university college of commerce for the learning opportunity and Dr. Kuo, Biing-shen, my advisor, for his valuable guidance on this thesis; Dr. Lin, Shin-ju who provided me the fundamental knowledge on managerial economics, business forecasting and many valuable comments as the thesis Supervisor Committee member; and Dr. Tsay, wen-jen of Academia Sinica, who is also on the Supervisor Committee, for his valuable suggestions and comments.

Special thanks to my friends, colleagues, and classmates who provided me with many valuable input and support regarding the thesis work. Finally, I am grateful to the support from my family throughout the years.

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REFERENCES

1. J. Holton Wilson and Barry Keating and John Galt Solution Inc., “Business Forecasting with Accompanying Excel-based ForecastX™ Software”, 4th edition, 2002, from McGraw-Hill Irwin, U.S.A..

2. BSRIA, “China Report 18768”, 2005, from Building Service Research and Information Association, UK (www.bsria.org.uk)

3. NBS, “Yearbook”, 1995~2005, from National Bureau of Statistics of China (www.stats.gov.cn)

4. Wikipedia, “GDP data published by the People’s Republic of China 1952~2004”, Compiled by Hitotsubashi University from Japan and World Bank.

5. Jiang Zemin’s Report at 16th Party Congress, “Build a Well-off Society in an All-Round Way and Create a New Situation in Building Socialism with Chinese Characteristics”, Nov. 2002, Xinhua News Agency, from Xinhua Online (www.xinhuanet..com)

6. Lang Siwei(郎四維), “Energy Efficiency Design Standards for Buildings”, Nov. 2004, Kuming China, from China Building Energy Efficiency Association.(中國建築業協會建築節能發展委員會)

7. Tsai D.C.(蔡德奇), Wu Y.S.(吴元兴),Cheng C.F.(郑志锋), “ Report on Index System Research on Archiving the Harmonious Society” (全面建设小康社会指标体系研究)))), Aug. 2003, from Development Research Center of State Council, China (www.drc.gov.cn)

8. Long Weiding(龍維定), “The Prospect of China Air Conditioning in Residential Buildings”(中國民用建築空調業的發展前景) November 2005, Department of Facilities

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Engineering and Management, Tongji University, Shanghai(上海同濟大學), from Chinese Electron Electric Appliance Net (www.cnele.com)

9. David Fridly, Gregory Rosenquist, Jiang Lin, Li Aixian, Xin Dingguo, Cheng Jianhong, “Technical and Economical Analysis of Energy Efficiency of China Room Air Conditioner”, Feb. 2001, from Energy Analysis Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, U.S.A.; China National Institute of Standardization (CNIS), Beijing, China; Beijing Energy Efficiency Center (BECon), Beijing, China.

10. Jiang Lin, Gregory Rosenquist, “China Cooled with Tighter RAC Standard”, 2005, from Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, U.S.A

11. “Ductless AC Future EER Requirement” (無風管冷氣機能源效率比基準), Jan. 2006, from Bureau of Energy, Ministry of Economic Affairs, R.O.C. (www.moeaec.gov.tw/laws/saveenergy)

12. Danish Energy Management, “Benchmarking of Air Conditioner Efficiency Levels in Five Asia Countries” prepared for the Australian Greenhouse Office, June 2004, from APEC ESIS (Asia Pacific Economic Corporation Energy Standard Information System (www.apec-esis.org)

13. State Grid Corporation of China (SGCC), “2005 China Power Supply Report”, March 2005, from Asia Consulting Network (www.acunion.net)

14. Long Weiding, Ting Zhong, Beihong Zhang, “China: The Issue of Residential Air Conditioning”, April 2004, from Department of Facilities Engineering and Management, Tongji University, Shanghai, China

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15. Lee Y.H. ( 李櫻穗), Lin Y.T. (林佑庭), “2020 China Energy Demand and Ocean Freight Safety Trend”, Feb. 2006, from Air Force Journal-585, Ministry of National Defense, R.O.C. (www.mnd.gov.tw)

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C h a p t e r 1

INTRODUCTION

1.1. Background Taiwan experienced alternate electrical power black out in the 90’s during high economic growth. Taiwan Power Company imposed the world’s most stringent EER code on Window unit to help reduced summer time peak demand due to air conditioning load, through the Industrial Development Bureau, Ministry of Economics Affairs in 1993. The residential and light commercial unitary application represents some 90% of the total Taiwan Air Conditioning market. Most of these applications used room air conditioner (RAC) and package air conditioner (PAC). High EER code not only helped ease the peak load demand on power supply but also advanced local manufacturing technology. Thus has led Taiwan made Window unit export to developed countries such as Japan and US. Today, the higher EER requirement helped prevent low cost China made Window unit import to Taiwan market.

The rapid economic growth has also lead to power supply shortage in China. Beginning at 2001, Shanghai and Suzhou implemented alternate power black out in the summer time. The situation did not improve due to the continuous strong growth. The situation becomes worse due to lack of incentive for the power company to build more power plants and for the consumer to shift peak load usage. Building new power plants only to meet peak load demand and idle at most other off peak hours, is a waste of capital investment. For China’s plan to build a harmonious society by year 2020, to sustain the growth, the energy and resource

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consumption must be compatible with the ability to supply. The goal is not only to improve the standard of living but also growth in an environmentally acceptable manner. There need to have a reliable forecasting method to arrive at a good electrical power demand estimation and to implement a technology feasible air conditioning EER code requirement. The effort is to find the balance of economic growth and environmental impact before one outgrowth the other.

1.2. Objectives The life time of a RAC (room air conditioner) and PAC (package air conditioner) is around 5~7 years. That is to say, if a new EER code implement on or before 2010, most of the residential and light commercial air conditioning will be running with the new EER standard by 2020. Thus, we can use the new EER to estimate RAC and PAC total electrical power demand at peak time which is 85~90% of total air conditioning power used. Air conditioning consumed 40%~55% of building energy and building energy consumed around 40% of total power supplied in developed country. Therefore, from the estimated total number of RAC+PAC, and EER code, we can derive the peak load demand on electrical power supply in total. Therefore, we can conclude that the higher the EER, the lower the demand.

The air conditioning market growth is related to a number of factors: 1) Traditionally, it grew at a close correlation with the nations GDP growth because the air conditioning industry itself is a part of the GDP; 2). Building floor area change; new buildings will create more air conditioning requirement; 3). Household disposable income (DPI) changes, to allow household to buy and install air conditioner for the improvement of living standard; 4). Households consumption expenditure, for its relation to the actual purchase of the home appliance; 5). Retail price changes, according to the supply and demand relationship, the lower price will increase the purchasing power; 6). Durable consumer goods ex-factory price indices

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have a relationship with the market demand to balance the supply. This study examined various factors, for its correlation with the total number of RAC+PAC - the dependent. By using quantitative method to analyze the data, thus derive a model for forecasting. The analysis was based on yearly data from 1995 to 2005, for forecasting total RAC+PAC quantity till year 2020.

The Excel-Based ForecastX™ software (FX) from the book “Business Forecasting” by J. Holton Wilson and Barry Keating and John Galt Solution Inc. (McGraw-Hill Irwin 4th edition) were used for the statistic analysis of the data and establishing the Model.(1)

The objective of this study is a decision-support application for planner and policymaker. It should help in the evaluation of options for dealing with China's future energy problems. The core of this application is an integrated analysis of China's air conditioning market prospects taking into account changes on GDP; new building introduced each year; household disposal income; selling price; plus other economic, and political dimensions. A range of related data sets were selected, and are merged into an integrated model.

China’s formidable economic growth leads to a multi-criteria environmental issue. It cannot be solved by dealing with single dimension only - such as by focusing on the problem of energy supply. There are several dimensions that play a major role in China's economic growth demand prospects: population growth; urbanization; industrialization; standard of living; supply of alternative energy resource; policy and technological developments. Each of these dimensions must be taken into account for political planning and scientific research concerning the issue. This study brings together data, analyses, and information resources for some of these dimensions.

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C h a p t e r 2

MODEL BUILDINGS

China air conditioning market is growing at an annual average rate of 12.8% in quantity since 2000, according to Building Services Research and Information Association (BSRIA) from UK (www.bsria.org.uk) in its “China 2005 (Report 18768)” (2). Household incomes, warmer summer each year, and a relatively low penetration (51% by 2004) of air conditioner helped sustain the growth. Of the total market size US$7.96 Billions (2004), 88.8% are residential and light commercial unitary market (US$7.06B). Residential unitary market or RAC includes window units (through the wall), movable (or portable) units and mini split below 5 kW (mostly single split). Light commercial unitary market or PAC includes package unit, single and multiple split from 5 kW to 17.58 kW, and US style rooftop, ductless split.

Except window and package unit, the number of Out Door units (OD) is used for market size calculation, single or multiple split. Out door unit includes refrigeration compressor and condenser fan that consumed electrical power to produce cooling during hot season or heating during cold season. The number of Indoor Units (ID) for multiple split will not be considered as it contain only a fractional fan motor (<1 hp), to deliver cooled air, consumed very little electrical power while running.

2.1. The Dependent Factor: The dependent factor is the sum of RAC and PAC (RAC+PAC) in quantity of OD. The average size RAC is “3.5 kW” unit (also called the cost control model for financial model simulation purpose), while PAC average size is “17.58kW” unit. China market Window unit sale peaked during 1997 to 1.5 millions sets. Taiwan

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market Window unit peak at 0.7 millions in the mid ‘90. China Window units continue to decline since 1997, replaced mainly by the equivalent size mini split. The reasons are: 1). Economy: Mini-split reached the scale economy much faster than Window unit due to competitive pricing; 2). Superior performance: Low noise and better appearance than window unit. China produced 42 millions ID in 2005, is the world’s largest producer, from some 50 local and foreign brands. According to BSRIA, 59% of the total produced is for export market. Other than package (self contain) unit, PAC also includes the “Central residential unit system” (戶式中央空調系統 ). This is a small central air conditioning system for residential and light commercial application. Its capacity range is between 7kW to 40kW, can be applied to 60~500 square meter space, for large apartment or small stores. The US design roof top and Japanese design multiple split, and locally made small air-cooled water system are competing feverishly in this market. PAC application represents the most growth potential in China at least till 2010. The followings are data from BSRIA:

1995A 1996A 1997A 1998A 1999A 2000AAnnual

% change1995-2000

Window 1,228,571 1,376,000 1,500,000 1,340,000 1,165,200 1,107,000 -1.7%Portable/Moveable 32,800 41,000 50,000 44,000 35,100 33,400 0.3%Minisplits/ductless splits -

<5kW/RAC 2,825,600 3,108,160 3,462,600 3,600,930 7,149,000 8,140,000 19.3%Cooling only 847,680 932,448 1,038,780 1,080,279 2,144,700 2,442,000 19.3%

Heat pump 1,977,920 2,175,712 2,423,820 2,520,651 5,004,300 5,698,000 19.3%

>5kW/PAC 385,309 423,840 517,400 538,070 1,007,700 1,225,600 21.3%Cooling only 115,593 127,152 155,220 161,421 302,310 367,680 21.3%

Heat pump 269,716 296,688 362,180 376,649 705,390 857,920 21.3%

Total ductless splits 3,210,909 3,532,000 3,980,000 4,139,000 8,156,700 9,365,600 19.5%

Grand Total 4,472,2814,472,2814,472,2814,472,281 4,949,0004,949,0004,949,0004,949,000 5,530,0005,530,0005,530,0005,530,000 5,523,0005,523,0005,523,0005,523,000 9,357,0009,357,0009,357,0009,357,000 10,506,00010,506,00010,506,00010,506,000 15.3%

China GDP China Statistics 10.9 10 9.3 7.8 7.6 8.4 9.0

Note: 1996~1998 window Source: BSRIA

1995~1998 mini/ductless splitCalculating according to corresponding percentage from 1999 to 2001 & from 2003 to 2005

1995 Calculating according to the growth rate from 1999 to 2001 & from 2003 to 2005

CHINA

Residential (RAC) / light commercial (PAC) unitary market, volume of outdoor units

Both of data is from BSRIA, but the continual growth rate(yoy) is nearly100%. Please refer it in care.

Table 2-1: Indicate 1995~2000 RAC/PAC Market Actual from BSRIA.

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2001A 2002A 2003A 2004A 2005A 2006EAnnual

% change2001-2006

Window 1,018,000 937,000 890,000 712,000 540,000 455,700 -12.5%Portable/Moveable 30,000 87,000 90,000 80,000 78,400 76,800 17.0%Minisplits/ductless splits -

<5kW/RAC 8,548,000 11,070,000 12,731,000 15,500,000 17,700,000 19,215,200 14.5%Cooling only 2,992,000 3,321,000 3,819,300 4,500,000 5,200,000 5,514,560 10.7%

Heat pump 5,556,000 7,749,000 8,911,700 11,000,000 12,500,000 13,700,640 16.2%

>5kW/PAC 1,305,000 1,539,000 1,769,000 2,230,000 2,420,600 2,711,250 13.0%Cooling only 457,000 461,700 530,700 680,000 720,000 780,500 9.3%

Heat pump 848,000 1,077,300 1,238,300 1,550,000 1,700,600 1,930,750 14.7%

Total ductless splits (PAC) 9,853,000 12,609,000 14,500,000 17,730,000 20,120,600 21,926,450 14.3%

Grand Total (RAC+PAC) 10,901,000 13,633,000 15,480,000 18,522,000 20,739,000 22,458,950 12.8%

GDP China Statistics 8.3 9.1 10 10.1 10.2 9 9.5

Note: Source: BSRIA

CHINA


Table2-2: Indicate 2001~2005 RAC/PAC Actual and 2006 Estimate from BSRIA

2005A 2006E 2007E 2008E 2009E 2010EAnnual

% change2005-2010

Annual% change2005-2009

Window 540,000 455,700 364,500 291,600 250,000 210,000 -14.6% -14.3%Portable/Moveable 78,400 76,800 75,300 73,800 70,000 67,000 -2.6% -2.2%Minisplits/ductless splits -

<5kW/RAC 17,700,000 19,215,200 20,646,010 22,435,400 24,631,000 26,400,000 6.9% 6.8%Cooling only 5,200,000 5,514,560 5,846,010 6,230,620 6,830,620 7,400,000 6.1% 5.6%

Heat pump 12,500,000 13,700,640 14,800,000 16,204,780 17,800,380 19,000,000 7.2% 7.3%

>5kW/PAC 2,420,600 2,711,250 3,051,000 3,226,120 3,336,120 3,590,000 6.8% 6.6%Cooling only 720,000 780,500 839,300 906,120 936,120 990,000 5.5% 5.4%

Heat pump 1,700,600 1,930,750 2,211,700 2,320,000 2,400,000 2,600,000 7.3% 7.1%

Total ductless splits (PAC) 20,120,600 21,926,450 23,697,010 25,661,520 27,967,120 29,990,000 6.9% 6.8%

Grand Total (RAC+PAC) 20,739,000 22,458,950 24,136,810 26,026,920 28,287,120 30,267,000 6.5% 6.4%

GDP China Statistics 10.2 9 9 8 8 7 8.5

Note: Source: BSRIA estimation

CHINA


Table 2-3: Indicate RAC/PAC Market Projection till 2010 from BSRIA.

Total number of RAC+PAC is shown on the following tables. Table 1-1 indicated total number of RAC+PAC from 1995 to 2000 and the annual percentage change during that period is 15.3%. Table 1-2 indicate the same from 2001 to 2006E (estimation) at 12.8% annual percentage change. Table 1-3 is the BSRIA estimation from 2006 to 2010 at 6.5% annual percentage change.

In summary:

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Period 1995 1996 1997 1998 1999 2000BSRIA: RAC+PACTotal OD (qty)

4,472,280 4,949,000 5,530,000 5,523,000 9,357,000 10,506,000

Period 2001 2002 2003 2004 2005 2006BSRIA: RAC+PACTotal OD (qty)

10,901,000 13,633,000 15,480,000 18,522,000 20,739,000 Table 2-4 RAC+PAC total in quantity 1995~2005 from BSRIA

2.2. The Independent Factors: Factor that may have influenced the growth of RAC+PAC from 1995 to 2005 in China includes the followings: � Gross Domestic Products (GDP) � Floor Space of Buildings Under Construction and Completed � Per Capital Annual Disposal Income of Households � Household Consumption Expenditure � Retail Price Index � Ex-factory Price Indices of Durable Consumer Goods

Data from the National Bureau of Statistics of China (www.stats.gov.cn) Yearbook(3) were adopted for the analysis. We will examine these factors one by one. 2.2.1. Gross Domestic Products (GDP): China experienced sharp GDP increase after the reform begins in 1978. GDP has risen from Rmb362.4 billion to Rmb18.232 trillions in 2005 (both figures at current prices), more than 50 times in 27 years. Per capital GDP rising from Rmb379 to Rmb10,561.

The following are (from Wikipedia) scatter graph of the People’s Republic of China’s GDP from 1952 to 2005, based on nominal GDP data published by the People’s Republic of China and compiled by Hitotsubashi University (Japan) and confirm by the economic statistic indicator from the World Bank.(4)

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Chart 2-1: China GDP Growth from 1952 to 2005, excerpt from Wikipedia

Gross Domestic Product (GDP) refers to the final products at market prices produced by all resident units in a country during a certain period of time. In the practice of national accounting, gross domestic product is calculated with three approaches, i.e. production approach, income approach and expenditure approach, which reflect gross domestic product and its composition from different aspects. Air conditioning industry itself is a part of the GDP, we will examine the correlation between GDP and Per Capital GDP with the RAC+PAC growth.

In summary: Period 1995 1996 1997 1998 1999 2000

China Statistics:GDP (100 mm yuan)

58,478.1 67,884.6 74,462.6 78,345.2 82,067.5 89,468.1

Period 2001 2002 2003 2004 2005 2006


97,314.8 105,172.3 117,390.2 136,875.9 149,879.0 Table 2-5 Gross Domestic Products from China Statistics Yearbook Chart 3-1.

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Per Capital GDP summary: Period 1995 1996 1997 1998 1999 2000

China Statistics: PerCapital GDP

(yuan/person)4,854.00 5,576.00 6,054.00 6,308.00 6,551.00 7,085.74

Period 2001 2002 2003 2004 2005 2006

China Statistics: PerCapital GDP

(yuan/person)7,651.44 8,214.02 9,111.00 10,561.00 12,041.00

Table 2-6 Per Capital Gross Domestic Products from China Statistics Yearbook Chart 3-1.

China’s Bureau of Statistics made GDP Adjustment on Feb. 28, 2006. 1993199319931993----2004 GDP Adjustment 2004 GDP Adjustment 2004 GDP Adjustment 2004 GDP Adjustment

Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Historical 13.50% 12.60% 10.50% 9.60% 8.80% 7.80% 7.10% 8.00% 7.50% 8.30% 9.50% 9.50%

Adjustment 14.00% 13.10% 10.90% 10.00% 9.30% 7.80% 7.60% 8.40% 8.30% 9.10% 10.00% 10.10% Table 2-7: China GDP Adjustment by the Statistics Bureau on Feb. 28, 2006

Period 1995 1996 1997 1998 1999 2000


58,478.1 67,884.6 74,462.6 78,345.2 82,067.5 89,468.1

Period 2001 2002 2003 2004 2005 2006


109,655.0 120,333.0 135,823.0 159,878.0 182,321.0 Table 2-8: Revised Gross Domestic Products by the Statistics Bureau, Feb. 28, 2006

2.2.2. Floor Space of Buildings Under Construction and Completed In the newly developed market such as China, the Air Conditioning businesses grow hand in hand with the construction industry. We will examine floor space of building both under construction and completed for its correlation with RAC+PAC growth. The time lags between building construction begin and installation of air conditioners should also be considered.

- 10 -

Floor Space of Buildings under Construction refers to floor space of buildings under construction during the reference period, including newly started buildings, buildings started earlier and continued during the reference period, and buildings suspended earlier but restarted during the reference period, buildings completed during the reference period, and buildings under construction and then suspended during the reference period. In summary:

Period 1995 1996 1997 1998 1999 2000

China Statistics: FloorSpace of Buildings UnderConstruction (10000 m²)

89,862.80 129,087.00 128,680.30 137,593.60 147,262.50 160,141.10

Period 2001 2002 2003 2004 2005 2006

China Statistics: FloorSpace of Buildings UnderConstruction (10000 m²)

188,328.70 215,608.70 259,377.13 291,938.52 328,600.00

Table 2-9 Floor Space of Building Under Construction from China Statistics Yearbook Chart 15-11.

Floor Space of Buildings Completed refers to the floor space of buildings that are completed in the reference period in accordance with the requirements of the design, up to the standard for putting them into use, and have been checked and accepted by concerned departments as qualified ones. In summary:

Period 1995 1996 1997 1998 1999 2000

China Statistics: FloorSpace of Buildings

Completed (10000 m²)35,666.30 60,047.90 62,244.00 65,682.60 73,924.90 80,714.90

Period 2001 2002 2003 2004 2005 2006

China Statistics: FloorSpace of Buildings

Completed (10000 m²)97,699.00 110,217.10 122,827.61 128,162.56 143,503.53

Table 2-10 Floor Space of Building Completed from China Statistics Yearbook Chart 15-11.

- 11 -

2.2.3. Per Capital Annual Disposal Income of Households In high land price countries such as Japan, Taiwan and Hong Kong, a large portion of the income is used for housing rental or loan, a relatively small portion of income is left for living standard improvement such as buying air conditioner. However, in China, most urban resident stay in the house provided by the government, therefore a relatively high portion of the income can be used for non-housing expenditure. Therefore, DPI may have more influence on purchasing power than GDP.

Comparing DPI between urban and rural household, the urban households is 2.71 times of the rural ones in 1995, while in 2004, the ratio increases to 3.2 times. Urban household DPI has more correlation with the air conditioner market than the rural one. Therefore, DPI of the urban households is used for this analysis.

Disposable Income of Urban Households refers to the actual income at the disposal of members of the households which can be used for final consumption, other non-compulsory expenditure and savings. This equals to total income minus income tax, personal contribution to social security and sample household subsidy for keeping diaries. Following formula is used: Disposable income = total household income - income tax - personal contribution to social security - sample household subsidy for keeping diaries In summary:

Period 1995 1996 1997 1998 1999 2000

China Statistics: PerCapital Annual Disposal

Income of UrbanHouseholds (yuan)

4,280.00 4,838.90 5,160.30 5,425.10 5,854.02 6,280.00

Period 2001 2002 2003 2004 2005 2006

China Statistics: PerCapital Annual Disposal

Income of UrbanHouseholds (yuan)

6,859.60 7,702.80 8,472.20 9,421.60 10,493.00

- 12 -

Table 2-11: Per Capital Annual Disposal Income of Urban Households from China Statistic Yearbook Chart 10-2

2.2.4. Household Consumption Expenditure: Household consumption expenditure represents the actual purchase on durable goods items such as air conditioners. Similar to household DPI, urban household consumption expenditure is 3.40 times of the rural ones in 1995, while in 2004, the ratio increased to 3.47. Therefore, the urban data are used for the analysis.

Consumption Expenditure of Urban Households refers to total expenditure of the sample households for consumption in daily life, including expenditure on eight categories such as food, clothing, household appliances and services, health care and medical services, transport and communications, recreation, education and cultural services, housing, miscellaneous goods and services. In summary:

Period 1995 1996 1997 1998 1999 2000

China Statistics: UrbanHouseholds Consumption

Expenditure (yuan)4,874.00 5,430.00 5,796.00 6,217.00 6,796.00 7,402.00

Period 2001 2002 2003 2004 2005 2006

China Statistics: UrbanHouseholds Consumption

Expenditure (yuan)7,761.00 8,047.00 8,473.00 9,105.00 10,194.83

Table 2-12 Urban Households Consumption Expenditures from China Statistic Yearbook Chart 3-18

2.2.5. Retail Price Index Retail Price Indices reflect the trend and degree of change in retail prices of commodities during a given period. The change in retail prices of commodities directly affect the living expenditure of urban and rural residents, government revenue, purchasing power of residents and the equilibrium of market supply and

- 13 -

demand, and the ratio of consumption to accumulation. Therefore, the retail price indices are useful to analyze the changes of the above economic activities. Since there is no index for household home appliance retail price before 2003, general index is used for the analysis. The following indices used preceding year as 100.

Period 1995 1996 1997 1998 1999 2000

China Statistics: GeneralRetail Price Indices

114.80 106.1 100.8 97.4 97.0 98.5

Period 2001 2002 2003 2004 2005 2006


99.2 98.7 99.9 102.8 101.90 Table 2-13-1 General Retail price index from China Statistic Yearbook Chart 9-7

Adjusting by using 1995 as base at 100, the data is converted as follows: Period 1995 1996 1997 1998 1999 2000


100.00 106.10 106.95 104.17 101.04 99.53

Period 2001 2002 2003 2004 2005 2006


98.73 97.45 97.36 100.09 101.99 Table 2-13-2 General retail price index adjusted, using 1995 as 100

However, from the supply and demand relationship, the price has an inverted relationship with dependent RAC+PAC, the lower the price the higher the number of air conditioner sold, adjusting by inverting the indices from above:

Period 1995 1996 1997 1998 1999 2000


1.00 0.94 0.94 0.96 0.99 1.00

Period 2001 2002 2003 2004 2005 2006


1.01 1.03 1.03 1.00 0.98 Table 2-13-3 General retail price index inverted, using 1995 as 1.0

This study will review the retail price indices in various forms, of its correlation with the RAC+PAC air-conditioning market growth.

- 14 -

2.2.6. Ex-factory Price Indices of Industrial Products Durable Consumer Goods: The ex-factory price indices of durable consumer goods are another indicator that may influence market changes. It may have a more direct impact on sales volume.

Ex-factory Price Indices of Industrial Products reflect the trend and degree of changes in general ex-factory prices of all industrial products during a given period, including sales of industrial products by an industrial enterprise to all units outside the enterprise, as well as sales of consumer goods to residents. It can be used to analyze the impact of ex-factory prices on gross output value and value-added of the industrial sector.

The following indices used preceding year as 100. Period 1995 1996 1997 1998 1999 2000

China Statistics: Ex-Factory Price Indices of

Durable ConsumerGoods

100.00 95.50 94.90 94.00 95.60 96.40

Period 2001 2002 2003 2004 2005 2006China Statistics: Ex-

Factory Price Indices ofDurable Consumer

Goods

95.30 94.74 95.61 96.20 95.50

Table 2-14 -1 Ex-factory Price indices of Durable Consumer Goods from China Statistic Yearbook Chart 9-12

Adjusting by using 1995 as base at 100, the data is converted as follows: Period 1995 1996 1997 1998 1999 2000

China Statistics: Ex-Factory Price Indices of

Durable ConsumerGoods

100.00 95.50 90.63 85.19 81.44 78.51



Goods

74.82 70.89 67.77 65.20 62.26

Table 2-14-2 Ex-factory Price indices adjusted, using 1995 as 100

- 15 -

However, from the supply and demand relationship, the price has an inverted relationship with dependent RAC+PAC, the lower the price the higher the number of air conditioner sold, adjusting by inverting the indices from above:



Goods

100.00 104.71 110.34 117.38 122.78 127.37



Goods

133.65 141.07 147.55 153.38 160.61

Table 2-14-3 Ex-factory Price indices inverted, using 1995 as 1.0

Two more additional closely related independents factors were reviewed during the analysis: � Urban population changes in percentage of total � Number of air conditioner owned by urban households

2.2.7. Urban population changes in percentage of total population: Between 1985~2004, China urbanization ratio increases steadily from 22% to 41.8%. The urbanization ratio is 50% on average for middle develop nation, while high income nation is 79%. From the urbanization development point of view, when the ratio is increase from 36% to 60%. The country is at the accelerating stage of its urbanization. China is at the stage of accelerating urbanization. It is estimated that the ratio will reach 58.7% by 2020, above the middle developed country level. The air conditioning market should be affected by this change greatly.

Urban populations refer to all people residing in cities and towns, while rural populations refer to population other than urban population.

In summary:

- 16 -

Period 1995 1996 1997 1998 1999 2000

China Statistics: UrbanPopulation (%)

29.00% 29.40% 31.90% 30.40% 30.90% 36.10%

Period 2001 2002 2003 2004 2005 2006

China Statistics: UrbanPopulation (%)

37.70% 39.00% 40.50% 41.80% 43.10%

Table 2-15 Urban Population ratio from China Statistic Yearbook Chart 2-3:

2.2.8. Number of Air Conditioner Owned by Urban Households at Year-end: Research indicated that there is a correlation between GDP and number of air conditioner owned per household. It is reported that when the per capital income reached US$4,000~$4,500., there will be one air conditioner per household. China has more population live in the subtropical zone any other nation in the world. This may represent higher than average number of air conditioner per household. Analysis by Professor Long, Wei Ding of Shanghai Tong ji University showed DPI and electrical power consumption have a correlation with the number of air conditioner per household. Accordingly, by 2010 there will be 125.8 number of air conditioner per 100 households, and the number will reach 190 by 2020. In summary:

Period 1995 1996 1997 1998 1999 2000

China Statistics: Numberof Air conditioner Owned

Per 100 UrbanHouseholds at Year-end

(qty)

8.09 12.00 16.00 20.00 24.48 30.80

Period 2001 2002 2003 2004 2005 2006

China Statistics: Numberof Air conditioner Owned

Per 100 UrbanHouseholds at Year-end

(qty)

35.80 51.10 61.79 69.81 79.00

Table 2-16 Number of air conditioner owned per 100 urban households at year end from China Statistic Yearbook Chart 10-14.

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2.3 Data Examinations Validity of the various independent factors will be examined here.

2.3.1 Comparing RAC+PAC correlation of with GDP and Per Capital GDP GDP reflected total output growth while per capital GDP has the effect of dilution by the rural population. Using FX linear regression analysis, GDP R-square is higher, therefore GDP is used for the analysis.

DatesRAC + PAC Total (ODqty) - Forecast(LinearRegression)

Gross Domestic Product(100 mm yuan) -Forecast(LinearRegression)

Per Capital GDP(yuan/person) -Forecast(LinearRegression)

2020 42,524,320.00 326,917.97 19,720.76

AIC 342.65 238.68 175.22

BIC 343.45 239.48 176.02

Mean Absolute Percentage Error (MAPE) 12.77% 9.76% 6.62%

Sum Squared Error (SSE) 15,110,877,706,181.80 1,187,159,370.16 3,706,426.10

R-Square 95.28% 92.65% 92.31%

Adjusted R-Square 94.76% 91.83% 91.46%

Durbin Watson 1.02 0.45 0.48

Mean 10,873,843.64 105,337.82 7,637.02

Median 10,506,000.00 89,468.10 7,085.74

Standard Deviation 5,659,982.94 40,184.20 2,195.43

Variance 32,035,406,868,945.50 1,614,769,636.21 4,819,905.43 Table 2-17-1: Comparing GDP with Per Capital GDP using linear regression analysis with FX.

2.3.2 Comparing floor space of buildings under construction versus completed: Floor space of buildings under construction including newly started, buildings started earlier, and buildings restarted during the reference period, buildings completed during the reference period, and buildings suspended during the reference period. Standard office or apartment building takes 1~3 years to complete, therefore some of which may be duplicated year to year. Building completed has more correlation with the installation of air conditioner. It refers to the floor space

- 18 -

of buildings that are completed, up to the standard for putting them into use, for example, air conditioner installed and have been checked and accepted by concerned departments as qualified ones (i.e. permit issued).

Analyzing both data with linear regression method indicated floor space of building completed has a closer correlation with the air conditioning growth. R-square (97.48%) for floor space of building completed is greater than the R-square (92.85%) for floor space under construction. Therefore, floor space of building completed is use for further analysis.

Year

RAC + PAC Total

(OD qty) -

Forecast(Linear

Regression)

Floor Space of

Buildings under

construction

(10000 sq.m) -

Forecast(Linear

Regression)

Floor Space of

Buildings

Completed (10000

sq.m) -

Forecast(Linear

Regression)

AICAICAICAIC 342.65 252.45 223.08

BICBICBICBIC 343.45 253.24 223.87

Mean Absolute Percentage Error (MAPE)Mean Absolute Percentage Error (MAPE)Mean Absolute Percentage Error (MAPE)Mean Absolute Percentage Error (MAPE) 12.77% 10.18% 5.85%

Sum Squared Error (SSE)Sum Squared Error (SSE)Sum Squared Error (SSE)Sum Squared Error (SSE) 15,110,877,706,181.80 4,148,898,572.62 287,293,140.97

R-SquareR-SquareR-SquareR-Square 95.28% 92.85% 97.48%

Adjusted R-SquareAdjusted R-SquareAdjusted R-SquareAdjusted R-Square 94.76% 92.05% 97.20%

Durbin WatsonDurbin WatsonDurbin WatsonDurbin Watson 1.02 0.50 1.52

MeanMeanMeanMean 10,873,843.64 188,770.94 89,153.75

MedianMedianMedianMedian 10,506,000.00 160,141.10 80,714.90

Standard DeviationStandard DeviationStandard DeviationStandard Deviation 5,659,982.94 76,170.22 33,782.36

VarianceVarianceVarianceVariance 32,035,406,868,945.50 5,801,902,602.76 1,141,248,131.21 Table 2-17-2: Comparing Floor Space completed against under construction using linear regression analysis with FX.

2.3.3 Retail price index The commodity retail price indices changes are useful to analyze the living expenditures of urban and rural residents, government revenue, purchasing power of resident and the equilibrium of market supply and demand, and the ratio of

- 19 -

consumptions to accumulation. Since there is no index for air conditioner along, general index is used for the analysis.

The chart below, using exponential smoothing method, shows the retail price index changes.

China Static: 9-7 Retail price index (1995 = 100)China Static: 9-7 Retail price index (1995 = 100)China Static: 9-7 Retail price index (1995 = 100)China Static: 9-7 Retail price index (1995 = 100)

88

90

92

94

96

98

100

102

104

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

China Static: 9-7 Retail price index (1995 = 100) Forecast of China Static: 9-7 Retail price index (1995 = 100) Fitted Values Chart 2-2-1: Retail price index trend analysis using FX.

2.3.4 Urban population Urban populations refer to all people residing in cities or towns. Data are in percentage of total population. The number does not show strong correlation with the conditioner market change. R-square = 89.08%, Adjusted R-square = 87.86%. Using exponential smoothing method, the result is shown below:

China Static: 2-3 Urban population in percentage of total ()China Static: 2-3 Urban population in percentage of total ()China Static: 2-3 Urban population in percentage of total ()China Static: 2-3 Urban population in percentage of total ()

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

China Static: 2-3 Urban population in percentage of total () Forecast of China Static: 2-3 Urban population in percentage of total ()

Fitted Values Chart 2-2-2: Urban population trend analysis using FX.

- 20 -

2.3.5 Number of air conditioner owned per 100 urban household at year-end The number of air conditioner owned is actually the result of all the independent factors that influenced the dependent RAC+PAC. Although the exponential smoothing method indicated a good correlation, R-square = 97.74%, Adjusted R-square = 97.49%, this factor will not be used due to its lagging character.

China Statistics: 14-4 Number of Air conditioner Owned Per 100 Urban Households atChina Statistics: 14-4 Number of Air conditioner Owned Per 100 Urban Households atChina Statistics: 14-4 Number of Air conditioner Owned Per 100 Urban Households atChina Statistics: 14-4 Number of Air conditioner Owned Per 100 Urban Households at

Year-end (qty)Year-end (qty)Year-end (qty)Year-end (qty)

0

50

100

150

200

250

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

China Statistics: 14-4 Number of Air conditioner Ow ned Per 100 Urban Households at Year-end (qty)

Forecast of China Statistics: 14-4 Number of Air conditioner Ow ned Per 100 Urban Households at Year-end (qty)

Fitted Values Chart 2-2-3: Number of air conditioner owned trend analysis using FX..

2.4 RAC+PAC forecast: Double-exponential versus Liner-regression method: The results of both methods are shown here for future discussion and comparison. 2.4.1 RAC+PAC growth using double-exponential method:

BSRIA: RAC + PAC Total (OD qty)BSRIA: RAC + PAC Total (OD qty)BSRIA: RAC + PAC Total (OD qty)BSRIA: RAC + PAC Total (OD qty)

0

10000000

20000000

30000000

40000000

50000000

60000000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

BSRIA: RAC + PAC Total (OD qty) Forecast of BSRIA: RAC + PAC Total (OD qty) Fitted Values Chart 2-3-1: Double-exponential method forecast chart, using FX

- 21 -

Audit Trail - Statistics

Accuracy Measures Value Forecast Statistics ValueAIC 342.08 Durbin Watson 1.55BIC 342.88 Mean 10,873,843.64Mean Absolute Percentage Error (MAPE) 9.39% Median 10,506,000.00Sum Squared Error (SSE) 14,352,099,494,865.90 Standard Deviation 5,659,982.94R-Square 95.52% Variance 32,035,406,868,945.50Adjusted R-Square 95.02%

Method Statistics ValueMethod Selected Double Exponential Smoothing-HoltAlpha 0.34Gamma 1.00 Chart 2-3-2: Double-exponential method forecast audit trail, using FX Forecast -- Double Exponential Smoothing-Holt Selected

ForecastDate Annual

2007 23,003,819.232008 25,598,896.772009 28,193,974.312010 30,789,051.842011 33,384,129.382012 35,979,206.922013 38,574,284.462014 41,169,361.992015 43,764,439.532016 46,359,517.072017 48,954,594.612018 51,549,672.152019 54,144,749.682020 56,739,827.22Avg 39,871,823.23Max 56,739,827.22Min 23,003,819.23 Chart 2-3-3: Double-exponential method forecast smoothing-Holt, using FX

2.4.2 RAC+PAC growth using Liner Regression method: BSRIA: RAC + PAC Total (OD qty)BSRIA: RAC + PAC Total (OD qty)BSRIA: RAC + PAC Total (OD qty)BSRIA: RAC + PAC Total (OD qty)

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

40000000

45000000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

BSRIA: RAC + PAC Total (OD qty) Forecast of BSRIA: RAC + PAC Total (OD qty) Fitted Values Chart 2-4-1: Liner Regression method forecast chart, using FX

- 22 -


Accuracy Measures Value Forecast Statistics ValueAIC 342.65 Durbin Watson 1.02BIC 343.45 Mean 10,873,843.64Mean Absolute Percentage Error (MAPE) 12.77% Median 10,506,000.00Sum Squared Error (SSE) 15,110,877,706,181.80 Standard Deviation 5,659,982.94R-Square 95.28% Variance 32,035,406,868,945.50Adjusted R-Square 94.76%

Method Statistics ValueMethod Selected Linear Regression Chart 2-4-2: Liner Regression method forecast audit trail, using FX

Forecast -- Linear Regression SelectedForecast

Date Annual2007 20,868,730.912008 22,534,545.452009 24,200,360.002010 25,866,174.552011 27,531,989.092012 29,197,803.642013 30,863,618.182014 32,529,432.732015 34,195,247.272016 35,861,061.822017 37,526,876.362018 39,192,690.912019 40,858,505.452020 42,524,320.00Avg 31,696,525.45Max 42,524,320.00Min 20,868,730.91 Chart 2-4-3: Liner Regression method forecast, using FX 2.5 Building the best fitting model of RAC+PAC growth in China Using multiple regression method, all of the above factors, the data from 1995 to 2005, total of 11 years of data, will be fit to the model set as below.

RAC + PAC Total (OD qty) = C(1) + ( (Gross Domestic Product (100 mm yuan)) * C(2) ) + ( (Floor Space of Buildings Completed (10000 sq.m)) * C(3) ) + ( (Per Capita Annual Disposable Income of Urban Households (yuan) ) * C(4) ) + ( (Urban Household Consumption Expenditure (yuan) ) * C(5) ) + ( (Retail price index (1995 = 100 inverted)) * C(6) ) + ( (Ex-Factory Price Indices of Industrial Products Durable Consumer Goods (1995 = 100 inverted) ) *C(7) )

The chart below indicated the model present a nice fit of the data available.

- 23 -

RAC + PAC Total (OD qty)RAC + PAC Total (OD qty)RAC + PAC Total (OD qty)RAC + PAC Total (OD qty)

0

10000000

20000000

30000000

40000000

50000000

60000000

70000000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

RAC + PAC Total (OD qty) Forecast of RAC + PAC Total (OD qty) Fitted Values Chart 2-5-1 Multiple Regression method forecast chart, using FX

Audit Trail -- Coefficient Table (Multiple Regression Selected)Series Included Standard Overall

Descritpion in Model Coefficient Error T-test F-testDependent -24,368,300.81 7,635,155.18 -3.19 120.43Yes -134.06 86.13 -1.56Yes -28.29 48.85 -0.58

Yes 6,593.56 2,359.14 2.79

Yes 1,311.71 1,173.26 1.12

Yes 217,969.73 104,799.55 2.08Yes -186,460.18 149,865.27 -1.24

Per Capita Annual Disposable Income ofUrban Households (yuan)Urban Household ConsumptionExpenditure (yuan)Retail price index (1995 = 100 inverted)Ex-Factory Price Indices of Industrial

RAC + PAC Total (OD qty)Gross Domestic Product (100 mm yuan)Floor Space of Buildings Completed

Chart 2-5-2 Multiple Regression method forecast audit trail coefficient, using FX Audit Trail - Statistics

Accuracy Measures Value ValueAIC 317.02 3.21BIC 317.42 10,873,843.64Mean Absolute Percentage Error (MAPE) 3.49% 10,506,000.00Sum Squared Error (SSE) 1,763,600,492,229.38 5,659,982.94R-Square 99.45% 32,035,406,868,945.50Adjusted R-Square 98.62%

Method Statistics ValueMethod Selected Multiple Regression Chart 2-5-3 Multiple Regression method forecast audit trail statistics, using FX Forecast -- Multiple Regression Selected

ForecastDate Annual

2007 24,080,909.342008 27,122,155.802009 30,163,402.262010 33,204,648.722011 36,245,895.182012 39,287,141.642013 42,328,388.092014 45,369,634.552015 48,410,881.012016 51,452,127.472017 54,493,373.932018 57,534,620.392019 60,575,866.852020 63,617,113.31Avg 43,849,011.32Max 63,617,113.31Min 24,080,909.34 Chart 2-5-4 Multiple Regression method forecast, using FX

- 24 -

Year

RAC + PAC Total (OD

qty) - Forecast(Multiple

Regression)

Gross Domestic

Product (100 mm

yuan) -

Forecast(Double

Exponential

Smoothing-Holt)

Floor Space of

Buildings Completed

(10000 sq.m) -

Forecast(Double

Exponential

Smoothing-Holt)

Per Capita Annual

Disposable

Income of Urban

Households

(yuan) -

Forecast(Double

Exponential

Smoothing-Holt)

Urban Household

Consumption

Expenditure

(yuan) -

Forecast(Double

Exponential

Smoothing-Holt)

Retail price index

(1995 = 100

inverted) -

Forecast(Exponen

tial Smoothing)

Ex-Factory Price

Indices of

Industrial

Products Durable

Consumer Goods

(1995 = 100

inverted) -

Forecast(Double

Exponential

Smoothing-Holt)

Forecast 2020Forecast 2020Forecast 2020Forecast 2020 63,617,113.31 480,037.63 320,693.64 24,563.84 18,050.85 98.05 244.57

AIC 317.02 227.96 228.51 155.33 158.09 52.71 50.52BIC 317.42 228.75 229.30 156.12 158.88 53.11 51.31Mean Absolute Percentage Error (MAPE) 3.49% 5.21% 7.60% 3.20% 2.79% 1.90% 1.28%Sum Squared Error (SSE) 1,763,600,492,229.38 447,769,888.35 470,758,917.54 607,357.10 780,661.09 64.71 44.20R-Square 99.45% 97.23% 95.88% 98.48% 97.09% 34.43% 98.91%Adjusted R-Square 98.62% 93.07% 89.69% 96.21% 92.73% 0.00% 97.28%Durbin Watson 3.21 1.86 1.53 1.58 1.02 1.28 1.06Mean 10,873,843.64 105,337.82 89,153.75 6,799.13 7,281.44 98.89 128.99Median 10,506,000.00 89,468.10 80,714.90 6,279.98 7,402.00 99.91 127.37Standard Deviation 5,659,982.94 40,184.20 33,782.36 2,001.29 1,638.98 3.14 20.15Variance 32,035,406,868,945.50 1,614,769,636.21 1,141,248,131.21 4,005,149.15 2,686,241.87 9.87 406.00 Chart 2-5-5 Multiple Regression method statistics summary, using FX

The Multiple Regression Model: RAC + PAC Total (OD qty) = -24,368,300.81 + ( (Gross Domestic Product (100 mm yuan)) * -134.06 ) + ( (Floor Space of Buildings Completed (10000 sq.m)) * -28.29 ) + ( (Per Capita Annual Disposable Income of Urban Households (yuan) ) * 6,593.56 ) + ( (Urban Household Consumption Expenditure (yuan) ) * 1,311.71 ) + ( (Retail price index (1995 = 100 inverted)) * 217,969.73 ) + ( (Ex-Factory Price Indices of Industrial Products Durable Consumer Goods (1995 = 100 inverted) ) * -186,460.18 )

This model has R-sq = 99.45% and Adjusted R-sq of 98.62% (Chart 2-5-3), which is adequate for business forecasting purpose. Regarding the coefficient of the independent factors, the different scale of each coefficient does not imply the level of their impact to the output variable. The contribution of each independent factor to the trend can not be drawn directly here. Some “T-test” values may indicate low eminency that can be regarded as insignificant. (E.g. T-test for GDP =-0.56. Chart 2-5-2). However, the R-Sq of Retail price index is 34.79%, indicating weak correlation.

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2.6 Building the best fitting model of RAC+PAC growth in China using logarithm data with the Multi-regression method.

2.6.1 Taking logarithm of dependent RAC+PAC and all the independent factors

Period(year)

RAC + PACTotal (OD qty)Log

GrossDomesticProduct (100mm yuan) Log

Floor Space ofBuildingsCompleted (10000sq.m) Log

Per CapitaAnnualDisposableIncome of UrbanHouseholds(yuan) Log

Urban HouseholdConsumptionExpenditure(yuan) Log

Retail priceindex (1995 =100 inverted)Log

Ex-Factory PriceIndices ofIndustrialProducts DurableConsumer Goods(1995 = 100

1995 6.6505 4.7670 4.5523 3.6317 3.6879 2.0000 2.0000

1996 6.6945 4.8318 4.7785 3.6847 3.7348 1.9743 2.0200

1997 6.7427 4.8719 4.7941 3.7127 3.7631 1.9708 2.0427

1998 6.7422 4.8940 4.8175 3.7344 3.7936 1.9823 2.0696

1999 6.9711 4.9142 4.8688 3.7675 3.8323 1.9955 2.0891

2000 7.0214 4.9517 4.9070 3.7980 3.8693 2.0021 2.1051

2001 7.0375 5.0400 4.9899 3.8363 3.8899 2.0055 2.1260

2002 7.1346 5.0804 5.0422 3.8866 3.9056 2.0112 2.1494

2003 7.1898 5.1330 5.0893 3.9280 3.9280 2.0116 2.1689

2004 7.2677 5.2038 5.1078 3.9741 3.9593 1.9996 2.1858

2005 7.3168 5.2608 5.1569 4.0209 4.0084 1.9914 2.2058 Table 2-18 Dependent and independent factors in logarithm.

2.6.2 Using pro-cast to examine data after logarithm

Dates

RAC + PAC Total(OD qty) Log -Forecast(DoubleExponentialSmoothing-Holt)

Gross DomesticProduct (100 mmyuan) Log -Forecast(Holt-Winters)

Floor Space ofBuildingsCompleted (10000sq.m) Log -Forecast(DoubleExponentialSmoothing-Holt)

Per Capita AnnualDisposable Incomeof UrbanHouseholds (yuan)Log -Forecast(DoubleExponentialSmoothing-Holt)

Urban HouseholdConsumptionExpenditure (yuan)Log -Forecast(Holt-Winters)

Retail price index(1995 = 100inverted) Log -Forecast(Exponential Smoothing)

Ex-Factory PriceIndices ofIndustrial ProductsDurable ConsumerGoods (1995 =100 inverted) Log- Forecast(DoubleExponentialSmoothing-Holt)

2020 Forecast 8.29 5.92 5.81 4.56 4.43 1.99 2.47

AIC -29.42 -45.97 -29.23 -57.05 -55.93 -66.45 -74.01

BIC -28.63 -44.77 -28.43 -56.26 -54.74 -66.05 -73.21

Mean Absolute Percentage Error (MAPE)0.58% 0.36% 0.75% 0.31% 0.31% 0.41% 0.25%

Sum Squared Error (SSE) 0.03 0.01 0.03 0.00 0.00 0.00 0.00

R-Square 94.61% 97.76% 90.31% 98.40% 97.66% 33.98% 98.86%

Adjusted R-Square 94.01% 97.20% 89.24% 98.22% 97.07% 33.98% 98.73%

Durbin Watson 1.84 1.84 1.70 1.92 1.68 1.28 1.29

Mean 6.98 5.00 4.92 3.82 3.85 1.99 2.11

Median 7.02 4.95 4.91 3.80 3.87 2.00 2.11

Standard Deviation 0.24 0.16 0.18 0.12 0.10 0.01 0.07

Variance 0.06 0.03 0.03 0.02 0.01 0.00 0.00 Chart 2-6-1 Logarithm Pro-cast method statistics summary, using FX

The retail price indices remain the one with weaker correlation as indicated by the low R-sq value.

2.6.3 Using linear regression method to examine data after logarithm:

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Dates

RAC + PAC Total(OD qty) Log -Forecast(LinearRegression)

Gross DomesticProduct (100 mmyuan) Log -Forecast(LinearRegression)

Floor Space ofBuildingsCompleted (10000sq.m) Log -Forecast(LinearRegression)

Per Capita AnnualDisposable Incomeof UrbanHouseholds (yuan)Log -Forecast(LinearRegression)

Urban HouseholdConsumptionExpenditure (yuan)Log -Forecast(LinearRegression)

Retail price index(1995 = 100inverted) Log -Forecast(LinearRegression)

Ex-Factory PriceIndices ofIndustrial ProductsDurable ConsumerGoods (1995 =100 inverted) Log- Forecast(LinearRegression)

Forecast 2020 8.33 5.90 5.92 4.53 4.42 2.04 2.50

AIC -36.18 -48.43 -34.87 -61.83 -65.97 -63.66 -94.25

BIC -35.39 -47.64 -34.07 -61.04 -65.17 -62.87 -93.45

Mean Absolute Percentage Error (MAPE) 0.40% 0.37% 0.61% 0.27% 0.22% 0.51% 0.11%

Sum Squared Error (SSE) 0.02 0.01 0.02 0.00 0.00 0.00 0.00

R-Square 97.08% 97.85% 94.20% 98.96% 98.87% 29.10% 99.82%

Adjusted R-Square 96.76% 97.61% 93.55% 98.85% 98.75% 21.23% 99.80%

Durbin Watson 2.17 0.77 1.84 0.61 1.03 1.00 1.07

Mean 6.98 5.00 4.92 3.82 3.85 1.99 2.11

Median 7.02 4.95 4.91 3.80 3.87 2.00 2.11

Standard Deviation 0.24 0.16 0.18 0.12 0.10 0.01 0.07

Variance 0.06 0.03 0.03 0.02 0.01 0.00 0.00 Chart 2-6-2 Logarithm Linear Regression method statistics summary, using FX

2.6.4 Using multiple regression method, include all of the factors above, the data from 1995 to 2005, will be fit to the model set as below:

RAC + PAC Total (OD qty) LogRAC + PAC Total (OD qty) LogRAC + PAC Total (OD qty) LogRAC + PAC Total (OD qty) Log

0

1

2

3

4

5

6

7

8

9

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

RAC + PAC Total (OD qty) Log Forecast of RAC + PAC Total (OD qty) Log Fitted Values Chart 2-7-1 Logarithm Multiple Regression method forecast chart, using FX Forecast -- Multiple Regression SelectedForecast -- Multiple Regression SelectedForecast -- Multiple Regression SelectedForecast -- Multiple Regression Selected

ForecastForecastForecastForecast

DateDateDateDate AnnualAnnualAnnualAnnual Annual ActualAnnual ActualAnnual ActualAnnual Actual

2007 7.41 25,504,561

2008 7.48 29,998,397

2009 7.55 35,284,034

2010 7.62 41,500,987

2011 7.69 48,813,351

2012 7.76 57,414,133

2013 7.83 67,530,351

2014 7.90 79,429,019

2015 7.97 93,424,201

2016 8.04 109,885,297

2017 8.11 129,246,793

2018 8.18 152,019,734

2019 8.25 178,805,207

2020 8.32 210,310,209

AvgAvgAvgAvg 7.86 73,238,443

MaxMaxMaxMax 8.32 210,310,209

MinMinMinMin 7.41 25,504,561 Chart 2-7-2 Logarithm Multiple Regression method forecast, using FX

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Audit Trail -- Coefficient Table (Multiple Regression Selected)

Series Included Standard Overall

Descritpion in Model Coefficient Error T-test Elasticity F-test

Dependent -8.23 2.84 -2.90 103.42

No

No

Yes 1.56 0.86 1.81 0.86

Yes 2.26 1.18 1.92 1.25

Yes 3.48 1.30 2.68 0.99

Yes -3.05 2.68 -1.14 -0.92

Per Capita Annual Disposable

Income of Urban HouseholdsUrban Household Consumption

Expenditure (yuan) LogRetail price index (1995 = 100

inverted) LogEx-Factory Price Indices of

Industrial Products Durable

RAC + PAC Total (OD qty) Log

Gross Domestic Product (100 mm

yuan) LogFloor Space of Buildings

Completed (10000 sq.m) Log

Chart 2-7-3 Logarithm Multiple Regression method audit trail coefficient, using FX Audit Trail - StatisticsAudit Trail - StatisticsAudit Trail - StatisticsAudit Trail - Statistics

Accuracy MeasuresAccuracy MeasuresAccuracy MeasuresAccuracy Measures ValueValueValueValue Forecast StatisticsForecast StatisticsForecast StatisticsForecast Statistics ValueValueValueValue

AIC -46.03 Durbin Watson 2.89

BIC -45.63 Mean 6.98

Mean Absolute Percentage Error (MAPE) 0.29% Median 7.02Sum Squared Error (SSE) 0.01 Standard Deviation 0.24

R-Square 98.57% Variance 0.06

Adjusted R-Square 97.62%

Method StatisticsMethod StatisticsMethod StatisticsMethod Statistics ValueValueValueValue

Method Selected Multiple Regression Chart 2-7-4 Logarithm Multiple Regression method audit trail statistics, using FX

RAC + PAC Total (OD qty) Log = -8.23 + ( (Per Capita Annual Disposable Income of Urban Households (yuan) Log ) * 1.56 ) + ( (Urban Household Consumption Expenditure (yuan) Log ) * 2.26 ) + ( (Retail price index (1995 = 100 inverted) Log) * 3.48 ) + ( (Ex-Factory Price Indices of Industrial Products Durable Consumer Goods (1995 = 100 inverted) Log ) * -3.05 )

The purpose of taking logarithm is to reduce the influence due to different scale. However, converting the forecast results (Chart 2-7-2), total RAC+PAC forecast by 2020 will be 210,310,209 in quantity. This equals to 16.7% CAGR for the next 15 years. This is unrealistically high. The actual growth from 1995 to 2000 is 15.3%, and 12.8% from 2000 to 2005 on average. The trend should slow down together with most other leading indicators such as GDP, building floor space, and DPI.

2.6.5 Differencing to induce stationarity: Differencing is a simple transformation to remove trend from the data.

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Since Yt = a + bXt + Ut, ΔYt = a’ + b’ΔXt + Ut

YearRAC + PAC Total

(OD qty) LogDifferencing

1995 6.6505

1996 6.6945 0.0066

1997 6.7427 0.0071

1998 6.7422 -0.0001

1999 6.9711 0.0328

2000 7.0214 0.0072

2001 7.0375 0.0023

2002 7.1346 0.0136

2003 7.1898 0.0077

2004 7.2677 0.0107

2005 7.3168 0.0067 Table 2-19-1 Differencing the logged data Using linear regression to analyze the result as follows:

Differencing Logged RAC+PACDifferencing Logged RAC+PACDifferencing Logged RAC+PACDifferencing Logged RAC+PAC

-0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

RAC+PAC Log Forecast of RAC+PAC Log Fitted Values Chart 2-7-5 Linear regression of the differencing data

Converting linear regression result after differencing, the 2020 RAC+PAC total become 189,529,686 (Table 2-19-2). It has a reducing effect of the trend by about 10% at the end of the 15 years forecast. However, it remains far exceeded the more realistic forecast number of 63,617,613 which will be 7.7% CAGR from 2006 to 2020.

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LinearRegression

YearConvert to

RAC+PAC in logForecast

RAC+PAC total

6.6505

0.009397 1998 6.71

0.009412 1999 6.78 5,973,249

0.009427 2000 6.84 6,919,758

0.009442 2001 6.90 8,029,258

0.009457 2002 6.97 9,331,955

0.009472 2003 7.04 10,864,040

0.009487 2004 7.10 12,668,940

0.009502 2005 7.17 14,798,863

0.009517 2006 7.24 17,316,678

0.009532 2007 7.31 20,298,228

0.009546 2008 7.38 23,835,169

0.009561 2009 7.45 28,038,470

0.009576 2010 7.52 33,042,726

0.009591 2011 7.59 39,011,489

0.009606 2012 7.66 46,143,875

0.009621 2013 7.74 54,682,753

0.009636 2014 7.81 64,924,928

0.009651 2015 7.89 77,233,823

0.009666 2016 7.96 92,055,308

0.009681 2017 8.04 109,937,490

0.009696 2018 8.12 131,555,512

0.009710 2019 8.20 157,742,689

0.009725 2020 8.28 189,529,686 Table 2-19-2 Converting linear regression results 2.6.6 Multiple Regression method taking out Retail Price Index, and DGP using

logarithm data: Chart 2-7-3 indicated Retail Price and GDP are not included in the model, to find out the forecast using multiple regression logarithm data without retail price index influence:

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RAC + PAC Total (OD qty) LogRAC + PAC Total (OD qty) LogRAC + PAC Total (OD qty) LogRAC + PAC Total (OD qty) Log

0

1

2

3

4

5

6

7

8

9

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

RAC + PAC Total (OD qty) Log Forecast of RAC + PAC Total (OD qty) Log Fitted Values Chart 2-8-1 Logarithm Multiple Regression Chart w/o Retail Price and GDP factor, using FX Forecast -- Multiple Regression Selected

ForecastDate Annual

2007 7.422008 7.492009 7.562010 7.632011 7.702012 7.772013 7.852014 7.922015 7.992016 8.062017 8.132018 8.202019 8.272020 8.34Avg 7.88Max 8.34Min 7.42 Chart 2-8-2 Logarithm Multiple Regression forecast w/o Retail price and GDP factor, using FX

Audit Trail -- Coefficient Table (Multiple Regression Selected)Series Included Standard Overall

Descritpion in Model Coefficient Error T-test Elasticity F-testDependent -1.47 1.46 -1.01 50.58Yes -0.31 0.41 -0.74 -0.22

Yes 0.36 1.02 0.36 0.20

Yes 1.32 1.60 0.82 0.73

Yes 1.66 2.90 0.57 0.50

Per Capita Annual Disposable Income of UrbanHouseholds (yuan) LogUrban Household Consumption Expenditure (yuan)LogEx-Factory Price Indices of Industrial Products DurableConsumer Goods (1995 = 100 inverted) Log

RAC + PAC Total (OD qty) LogFloor Space of Buildings Completed (10000 sq.m) Log

Chart 2-8-3 Logarithm Multiple Regression coefficient w/o Retail price and GDP factor, using FX

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Accuracy Measures Value Forecast StatisticsAIC -38.32 Durbin WatsonBIC -37.92 MeanMean Absolute Percentage Error (MAPE) 0.43% MedianSum Squared Error (SSE) 0.02 Standard DeviationR-Square 97.12% VarianceAdjusted R-Square 95.20%

Method Statistics ValueMethod Selected Multiple Regression Chart 2-8-4 Logarithm Multiple Regression statistics w/o Retail price and GDP factor, using FX

Neither Retail price index nor GDP will influence the result of the forecast.

In this Chapter, we have examined different method of quantitative forecasting, using various independent factors that influence the outcome of the dependent: the air conditioner market growth in China. We will follow by comparing forecast against other statistic data for its validity.

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C h a p t e r 3

COMPARISON WITH OTHER STATISTICS

3.1. The best fitting model of RAC+PAC growth in China We have concluded that the best fitting model of the air conditioning growth is as follows: 3.1.1 The best fitting model using multiple regression RAC + PAC Total = -24,368,300.81 + (GDP * -134.06) + ((Floor Space of Buildings Completed) * -28.29) + ((Urban Household Per Capita Annual DPI) * 6,593.56) + ((Urban Household Consumption Expenditure) * 1,311.71) + ((Retail Price Index) * 217,969.73) + ((Durable Consumer Goods Ex-Factory Price Indices) * -186,460.18)

Year

RAC + PAC Total(OD qty) -Forecast(MultipleRegression)

Gross DomesticProduct (100 mmyuan) -Forecast(DoubleExponentialSmoothing-Holt)

Floor Space ofBuildings Completed(10000 sq.m) -Forecast(DoubleExponentialSmoothing-Holt)

Per Capita AnnualDisposable Incomeof Urban Households(yuan) -Forecast(DoubleExponentialSmoothing-Holt)

Urban HouseholdConsumptionExpenditure (yuan)- Forecast(DoubleExponentialSmoothing-Holt)

Ex-Factory PriceIndices of IndustrialProducts DurableConsumer Goods(1995 = 100inverted) -Forecast(DoubleExponentialSmoothing-Holt)

2006 21,114,672 179,600 140,273.17 10,284.45 9,556.01 159.11

2007 24,080,909 203,586 153,812.74 11,477.43 10,755.97 166.60

2008 27,122,156 224,852 166,649.74 12,484.08 11,317.12 172.60

2009 30,163,402 246,117 179,486.73 13,490.72 11,878.26 178.60

2010 33,204,649 267,383 192,323.72 14,497.37 12,439.41 184.59

2011 36,245,895 288,648 205,160.71 15,504.02 13,000.55 190.59

2012 39,287,142 309,914 217,997.70 16,510.66 13,561.70 196.59

2013 42,328,388 331,179 230,834.70 17,517.31 14,122.84 202.59

2014 45,369,635 352,445 243,671.69 18,523.96 14,683.99 208.58

2015 48,410,881 373,710 256,508.68 19,530.61 15,245.13 214.58

2016 51,452,127 394,976 269,345.67 20,537.25 15,806.28 220.58

2017 54,493,374 416,241 282,182.67 21,543.90 16,367.42 226.58

2018 57,534,620 437,507 295,019.66 22,550.55 16,928.56 232.57

2019 60,575,867 458,772 307,856.65 23,557.19 17,489.71 238.57

2020 63,617,113 480,038 320,693.64 24,563.84 18,050.85 244.57 Table 3-1: The results of the forecast using the best fitting model:

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3.1.2 Comparing the growth rate of the past against the 2020 forecast from above: RAC+PAC forecast (qty): China 2005 RAC+PAC total OD quantity is : PV = 20,739,000 1995 PV = 4,472,280By 2020, if the total number of unit increase to: FV = 63,617,113 2005 FV = 20,739,000In 15 years: NPER = 15 NPER = 10Use the RATE formula: RATE(nper,pmt,pv,fv)The Compound growth rate is: RATE = 7.76% RATE = 16.58% GDP forecast (100 mm yuan): China 2005 GDP is : PV = 149,879 1995 PV = 58,478By 2020, if the total GDP increase to: FV = 408,038 2005 FV = 149,879In 15 years time: NPER = 15 NPER = 10Use the RATE formula: RATE(nper,pmt,pv,fv)The Compound growth rate is: RATE = 6.90% RATE = 9.87% Total Building Floor Space Completed forecast (10000 sq m): China 2005 floor space completed is : PV = 143,503.53 1995 PV = 35,666By 2020, if the total completed increase to: FV = 320,693.64 2005 FV = 143,504In 15 years time: NPER = 15 NPER = 10Use the RATE formula: RATE(nper,pmt,pv,fv)The Compound growth rate is: RATE = 5.51% RATE = 14.94% Urban Household Per Capital Annual DPI (Rmb yuan): China 2005 urban household annual DPI : PV = 10,493.00 1995 PV = 4,280.00By 2020, if the total DPI increase to: FV = 24,563.84 2005 FV = 10,493.00In 15 years time: NPER = 15 NPER = 10Use the RATE formula: RATE(nper,pmt,pv,fv)The Compound growth rate is: RATE = 5.83% RATE = 9.38% Urban Household Consumption Expenditure (Rmb yuan):

PV = 10,194.83 1995 PV = 4,874.00By 2020, if the total Expenditure increase to: FV = 18,050.85 2005 FV = 10,194.83In 15 years time: NPER = 15 NPER = 10Use the RATE formula: RATE(nper,pmt,pv,fv)The Compound growth rate is: RATE = 3.88% RATE = 7.66%

China 2005 urban household consumption expenditure :

Durable Consumer Goods Ex-Factory Price Index;

PV = 0.6226 1995 PV = 1.0000By 2020, if the price index change to: FV = 0.4089 2005 FV = 0.6226In 15 years time: NPER = 15 NPER = 10Use the RATE formula: RATE(nper,pmt,pv,fv)The Compound growth rate is: RATE = -2.76% RATE = -4.63%

China 2005 durable goods ex-factory price index :

On the right is CAGR comparison between 1995~2005 against the forecast.

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3.1.3 Comparing with other quantitative forecasting methods Multiple

Regression

Linear

Regression

Double-

exponential

Logged Multiple

Regression

Remark

2020 Forecast

RAC+PAC total 63,617,113 42,524,320 56,739,827 210,310,209

2005~2020

CAGR 7.67% 4.9% 6.94% 16.7%

Table 3-2 Summary of results using different forecasting method The result by linear regression method (CAGR=4.9%) seems to be too pessimistic comparing with the economy that grew GDP 9.87% annually in the past 10 years and projected to grow near 7% annually till 2020.

The result by logged multiple regression (CAGR=16.7%) seems unrealistic as explain earlier in paragraph 2.6.4.

3.2. China’s Plan to Build a Harmonious Society by 2020 There are numerous papers and articles relating to 2020 economic plan and statistical forecast and index issued by the government agency since 2003. The following paragraphs highlighted some of the related issue pertinent to our study.

3.2.1 China’s 11th Five Years Economic Development Plan In the course of reform since 1978 and rapid growth, inconsistencies between the country's economic and social progress increased. The inconsistencies mainly are on education and healthcare, and between urban and rural area. For example, in 1980, shortly after the launch of reform, the income Gini coefficient, which is a yardstick against the relative equality of income, was 0.33 but 2006 stands at 0.46 beyond the internationally accepted warning level of 0.43.

China has an economic development plan on a five years interval. 2006 is the beginning of its 11th five year plan (2006~2010). In 2002, China announced its longer range economic development plan to build a harmonious society by 2020.

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The aim is not only to improve the standard of living of the Chinese and a society composed mainly by the middle class but also to allow a sustainable grow putting energy resource and environmental impact into consideration.

3.2.2 Defining Middle Income Class in China After more than 20 years of reform, the concept of "middle income class" emerged. It has begun to shape a change of China's social structure. According to the calculation of the Chinese Academy of Social Sciences (CASS), the middle income class in China accounted for 15% in 1999. It rose by 1% annually to 19% (an olive shape population distribution) in 2003. It is expected that the middle income community would grow to 40% (an onion shape population distribution) by 2020. CASS estimated that 48.5% of urban residents hold Rmb150,000 to Rmb300,000 per household. The fact that the majority of the rural population makes poor income, except a fraction of farmers with relatively higher income, it can be said that about 19% of the Chinese population are in the category of "middle income class".

BNP PARIBAS PEREGRINE, defines China's "middle income class" as: well-educated professionals and white-collars with a yearly earning of Rmb25,000 to 30,000 per capita, i.e. Rmb75,000 to 100,000 a year per household, partake in the corporate decision making and management and engage in mental work. The population up to the above mentioned standard makes up about 13.5% of the population in China.

Song Linfei, an expert in social structure, argued that under the present situation in China, groups with Engel index below 40% and the minimum taxable line for individual income exceeding Rmb1,200 or the xiao-kang community, should be regarded as middle income class.

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Lu Xueyi, a famous sociologist described middle class as: "To keep a steady, harmonious, sustainable and healthy development of the society and economy, it is essential to create a larger mid-income population… Socially or economically speaking, the middle income class is the most stable one in a society. They generally enjoy a well-off life, having stable jobs, hold positive attitude toward the society and policies, and are loyal consumers. They are one of the market boosters.

3.2.3 China’s GDP grows 7% annual for 40 years China must maintain an annual growth rate of 7% for 40 years in order to meet the target of building a harmonious (xiao-kang) society across the country by 2020 according to Minister Xu Guanhua of Science and Technology (科技部徐冠華). The time span is between the late of 1970s, when reform began, till 2020, which is the last year that China must fulfill its goal of building a harmonious (xiao-kang) society in an all-round way puts forward by Jiang Zemin's in late 2002.

To meet the target of quadrupling the per capita GDP by the year 2020, China must maintains high R&D investment, to allow the contribution rate of science and technology to GDP growth to reach 60% from 39% in 2006. China’s per capita supply of energy, water and land resources is becoming more stringent and the problems of resources and environment are becoming increasingly pressing after many years of economic growth. Science and technological development must help solve these problems.

Working out the economic growth rate for the coming 20 years, it takes a yearly growth rate of 7.2 percent to meet the goal. The growth rate for the first tens years might be higher, at seven to eight percent, whereas that for the second decade will be kept at six to seven percent. In the past 13 years, China's annual GDP growth averaged 9.3 percent, Zeng Peiyan, minister in charge of the State Development Planning Commission estimated.

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3.2.4 China urbanization rate to reach 60% by 2020 According to Chou Baoxing, Vice Minister of Construction, 50% percent of the Chinese population will live in urban areas by 2010 and the figure will reach 60%. China's urbanization drive was too fast that the negative effects brought about by urbanization, including environmental problems, traffic congestion and inadequate social insurance for migrant worker should be addressed.

The three important yardsticks for judging if the urbanization drive is healthy are the level of energy and natural resources protection, respect for local history and culture and care for the low-income groups." In 2005, 41% of Chinese live in urban areas, according to Shi Nan, Secretary General of the Urban Planning Society of China. The urban areas have the capacity to accommodate 50 percent of the population by 2010, as long as migrants are evenly distributed between the country's 600 cities and 20,000 townships.

3.2.5 About “Xiao-Kang” Xiao-kang literally means "Small Well-Being." The term has a long history. Xiao-kang was first mentioned as a social state in the book of Song, in which people led a fairly comfortable life. It was in the book of Rite(禮), visualizing as a social mode that comes next to Great Harmony (Da Tong大同), or a perfect society. There is not an exact English equivalent to xiao-kang and that xiao-kang is less affluent than "well-off" but better off than freedom from want.

Deng Xiaoping used xiao-kang, a household term in China, to describe Chinese-style modernization and referred to it as a goal to be attained by the Chinese nation by the end of the 20th century. Deng Xiaoping’s concept of a 'xiao-kang family' is by the end of this century, per-capita GDP of 1,000 US dollars. By the end of the 20th century, the China had reached a xiao-kang standard of living. China’s GDP quadrupled from 1980 to reach Rmb2.8 trillion by year 2000. Jiang Zemin's puts

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forward yet another goal, by the year 2020, when the country's GDP is expected to exceed Rmb35 trillion, quadruple the 2000 figure.(5)

High rate of domestic savings; a rise in the average education level and improved quality; as well as a more efficient state-owned economy and improved private enterprise, join forces with market-oriented reforms to propel the growth of the Chinese economy. Dai Xianglong (央行戴湘龍), governor of China's central bank -- the People's Bank of China said, "China is capable of raising funds from diverse channels to sustain the building of a harmonious society."

3.2.6 China’s new standard on residential and office building energy-efficiency China has launched ambitious plans to make its residential and office buildings more energy-efficient in 2006(6). China announced three sets of new national standards: 1) regulations on energy saving for civil buildings, 2).standards for residential buildings and, 3).standards for technical evaluations of residential buildings. Another new guideline regarding the assessment of green buildings take effect on June 1, 2006. Though not a compulsory standard, it asks for higher standards in energy consumptions and other environment-related indices.

Both the Law on Energy Saving and the Law on Architecture are being revised to accommodate the new standards. A new decree on energy-saving for buildings is also high on the agenda of the China State Council. The effort to improve building energy-efficiency is important not only to China, also to the rest of the world. China is now the world's fourth largest economy and the second largest energy consumer. Over 30% of the world's coal, steel and cement were consumed by China market. Building energy consumption accounts for 30% of China’s total energy consumption. The figure will rise to 40% if energy consumption for manufacture and transportation of construction materials is considered. According to Jiang Yi, a

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professor in architecture at Tsinghua University, if nothing is done to improve the situation building energy consumption in China will double by 2020. To meet the new demand would require the building of more than 10 power stations the size of the Three Gorges power station, he said.

3.2.7 China to build more energy-efficient new buildings Household energy consumption, including heating, electricity, and water, accounted for one-third of the country's total, 95% of China's buildings are "highly energy-consuming". New buildings that use less energy are essential. New energy standard allow the government to better supervise new building construction. Energy saving building techniques includes better insulation and airtight windows. According to statistics, the cost of a building that saves 60% of the energy per unit cost 5%~7% more than the ordinary buildings. By 2020, China will transform all existing buildings into energy-saving ones. New buildings must embrace technology that could save 65% more energy per unit.

China is the world's biggest construction market. Half of the buildings built every year are located in China. According to experts, by 2020, China will see the construction of new buildings totaling 30 billion square meters in floor space. It would be an impossible mission to meet the energy requirement for lighting, heating and cooling these buildings if the current energy consumption model continues.

As of March 1, 2006, China began to attach energy labels to air-conditioners and refrigerators, indicating how much energy they consume. The label will be applied to more and more household appliances. By the joint efforts of the government and manufacturers, the consumer will pay more attention on energy saving.

3.2.8 China is to refurbish the Existing building to save energy

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China has begun a massive reconstruction of old buildings to make them more energy efficient, the Deputy Minister of Construction, Qiu Baoxing, said on March 2006. Work on buildings of the 10 central government ministries including the Ministry of Construction has begun and will be finished in one and a half year. If reconstruction on the first 10 buildings proves successful, all departments of the central government and provincial governments will follow suit. The refurbishment of old buildings is part of the government's bid to build a resource-saving and environmentally-friendly society.

The Chinese government has approved plans to reduce energy consumption of new urban buildings by 50% by the end of 2010. For large cities such as Beijing, it will be cut by 65%. By 2010, large cities will have 25% of their old private and public buildings refurbished for energy saving. Middle-sized cities will have 15% and small cities 10%, respectively. By 2020, all the old buildings in Chinese cities will be refurbished for energy saving.

3.3. Target 2020 Economy indices (7) Index Type Unit Base Y2000 Y2010 Y2020

Per Capital GDP USD 3030 854 1600 3050

Urban Per Capital DPI Rmb 18000 6280 12000 18000

Rural Per Capital Household Income Rmb 8000 2253 4000 7300

Urban 39.2 30 25 Engel Coefficient

Rural

% 40

49.1 40 35

Urban residence average floor space per person Sq. M 30 19 25 30

Urbanization ratio % 50 36.2 46 56

Ratio of households with computer % 20 4.2 10 20

College entrance rate % 20 11 20 30

Urban resident minimum living standard coverage % 70 30 50 70

Max. Crime rate % 0.1 0.2 0.15 0.1

Table 3-3 Economy Indices Target for 2020

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� Engel coefficient is the ratio of food expenditures versus total consumer

expenditures to reflect disposable income and standard of living changes. � Ratio of household with computer is an indicator of the e-level of the area. � College entrance rate is the ratio of people receiving higher education. � Urban resident minimum living standard coverage is the ratio of people covered

by the social security system such as pension, unemployment, medical, and labor insurance.

� Maximum crime rate is the maximum allowed criminal case per 10000 people.

3.4. Scenario Analysis of the China Air conditioning market Long, Wei Ding (上海同濟大學龍維定)of Shanghai Tong ji University forecasted total number of air conditioner in China will be 420 million in operation by 2020(8). In his article published on November 2005 title: The Prospect of China air conditioning in residential buildings (中國民用建築空調業的發展前景), indicated that there is a positive linear relationship between urban resident per capital disposal income (DPI), per capital electrical power consumption and the number of air conditioner per household. The coefficients are 0.9928 and 0.9681 separately. Therefore, there is a linear regression model with two dependents (DPI and electrical power consumption) with the dependent number of air conditioner per 100 urban households. The model forecasted 233 millions units air conditioner in China by 2010 at 125.8 units of air conditioner per 100 urban household. He also estimated total air conditioner sales will be 178.26 millions during 2004~2010 period, or 25 millions per year on average, based on the assumption that (residential) air conditioner life cycle will be less than 10 years.

The paper also predicted that the growth rate will slow down after the number of air conditioner reached one per household, therefore no longer a linear relationship. By

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2020, when per 100 urban household reached 190, total air conditioner in operation will reach 430 millions unit. This is similar to our forecast of 499 millions units total if we add up the forecast sales quantity from 2010~2020 assuming a 10 years life cycle of air conditioner.

Year

RAC + PAC Total(OD qty) -

Forecast(MultipleRegression)

AirConditionerSurvival rate

by 2020

Total Operational AirConditioner by 2020

2006 21,114,672 x 0 0

2007 24,080,909 x 0 0

2008 27,122,156 x 0 0

2009 30,163,402 x 0 0

2010 33,204,649 x 0 0

2011 36,245,895 x 0.1 3,624,590

2012 39,287,142 x 0.4 15,714,857

2013 42,328,388 x 0.7 29,629,872

2014 45,369,635 x 0.9 40,832,671

2015 48,410,881 x 1 48,410,881

2016 51,452,127 x 1 51,452,127

2017 54,493,374 x 1 54,493,374

2018 57,534,620 x 1 57,534,620

2019 60,575,867 x 1 60,575,867

2020 63,617,113 x 1 63,617,113

2011~2020 tot. 499,315,042 425,885,972 Table 3-4 Estimated Total Air Conditioner in Operation by 2020

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Chart 3-1: Survival Function of Air Conditioner

Table 5 indicate 2006~2020 total RAC+PAC forecast in China and total number of air conditioner sold between 2011~2020, using 7 years life time and a maximum life time of 10 years. Chart 1 indicated the “Survival function of Air conditioner”, assuming average air conditioner life is 12.5 years and all air conditioner are assumed to expire by their 16th years. Expert from “Technical and Economical Analysis of Energy Efficiency of China Room Air Conditioners” (9) by David Fridley, Gregory Rosenquist, Jiang Lin, Li Aixian, Xin Dingguo, and Cheng Jianhong, dates February 2001.

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C h a p t e r 4

THE APPLICATIONS

In this Chapter, the forecast results from previous chapter, that is total number of RAC+PAC by 2020 in China will be used to estimate the electrical peak load demand during summer time when all air conditioner is in operation to cool the room air and its impact on power supply. Most published paper or articles related to energy goals of 2020 are on the total electrical power consumption (on year round base) rather than peak demand. The energy goals is to limited the growth of energy consumption while quadrupled the GDP growth, only double the energy consumption. The total energy consumption goal has more to do with the resource availability, while the peak demand estimation has more to do with the installation of electrical power plant installation, the availability and reliability of power supply and better usage of the capital investment from the overall economy stand point. As the population and economy continue to grow and resource depleting, reliability and availability may soon become an even more critical basic requirement to our life. 4.1. EER requirement in China and elsewhere This paragraph will examine the EER or energy efficiency ratio requirement on different countries. EER is the ratio of energy output (capacity of the air conditioner) versus energy input (in the form of electrical power). EER can be displayed in different unit such as Watt per Watt; Btu/hour per Watt; or Kcal/hour per Watt. Watt is Joules per second. Joules is a unit of energy equal to one meter-kilogram.

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We will compare the minimum EER code requirement of China, against Taiwan, and some of the Asia Pacific Economic Corporation (APEC) nations, including US and Korea.

4.1.1 EER in China The mandatory Minimum Energy Performance Standard (MEPS) in China, effective January 2001 are as follows:

EER W/W Type Rated Cooling

(CC) W Cooling Only Heat Pump CC < 4500 2.20 2.15 Single-

Package CC > 4500 - - CC < 2500 2.50 2.40

2500 < CC < 4500 2.45 2.35 4500 < CC < 7100 2.40 2.30

Split CC > 7100 2.30 2.25

Table 4-1-1 China Room Air Conditioner Minimum Energy Performance Standards, September 2000 from APEC ESIS (Energy Standard Information System)

By October, 2005, China put in effect a mandatory labeling program classified the air conditioner’s energy efficiency into five categories:

Split Class 1 Class 2

2.9

below 4500W

2.6 2.4

2.7 2.5 2.33.1All sizes

2.53.2

Class 5

3.33.4 3.0

3.1 2.9 2.7

Residential Air Conditioner performance classfication (based on cooling COP or EER W/W),effective Oct. 2005

Single-package Class 1 Class 2 Class 3 Class 4 Class 5

larger than 7100W 3.2 3.0 2.8

2.8 2.64500W~7100W

Class 3 Class 4

Table 4-1-2 China Labeling Program

Class 1 is the best efficiency, indicating the future goal, Class 2 can be regarded as an energy efficient model, Class 3 and 4 is the minimum requirement by the national standard, and while Class 5 indicates the model to be obsolete soon.

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A study by Jiang Lin and Rosenquist(10) of the Lawrence Berkeley National Lab. USA reported that Class 2 the energy efficient unit will become the minimum standard by 2009 in China. Class 2 requirements will be a 20% energy efficiency improvement than the 2001 standard. 4.1.2 EER in Taiwan Unlike most other countries such as US and China, Taiwan’s EER code put much emphasis on Window unit, separating into three categories. The mandatory MEPS is based on testing standards CNS 3615, CNS 14464 (Sept. 12, 2001), effective January 2002:

EER W/W Type Rated Cooling

(CC) W Cooling Only Heat Pump CC < 2300 2.71 -

2300 < CC < 4100 2.77 - Single-Package CC > 4100 2.60 -

CC < 4100 2.97 - Split CC > 4100 2.73 -

Table 4-2: Taiwan Room Air Conditioner MEPS, Sept. 12, 2001

Taiwan’s Package terminal MEPS Requirements: � Air Cooled (power consumption > 3000W ) - minimum EER 2.84 (W/W) � Water Cooled - allowable minimum EER 3.69 (W/W)

Moving onward, Taiwan announced on January 6th, 2006(11) the longer range minimum energy performance standard for further strengthening the energy efficiency requirement of the air conditioner sold in this market. As the market relying more and more on imported products from Japan, Korea, China and APEC countries, the impact to local residential and light commercial air conditioner manufacturer is less significant as before.

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Stage Stage 1 Stage 2

Effective Date Jan. 1, 2011 to Dec .31, 2015 After Jan. 1, 2016

Type Rated Cooling (CC)(W) EER (w/w) CC< 2300 3.15 3.40 2300<CC<4100 3.20 3.45 4100<CC<7100 3.00 3.25

Single-package

7100<CC<10000 2.95 3.15 CC<4100 3.45 3.85 4100<CC<7100 3.20 3.55

Air-cooled

Split CC> 7100 3.15 3.40

Water-Cooled All types 4.25 4.80 Table 4-3: Taiwan’s RAC and PAC future EER requirements:

4.1.3 Japan’s EER requirement Japan does not have MEPS; instead it operates the Top Runner standards program. This program aims to dramatically improve energy efficiency of appliances by setting target values based on the current highest efficiency level of each type of product instead of the current average efficiency level. Manufacturers and importers have to ensure the average (sales weighted) efficiency of all their appliances meet this standard by a specified date (the target year). The program allows a continuum for improvement over time making manufacturers constantly increase the efficiency of appliances. The Top Runner standards are voluntary as there is no minimum level, however penalties can be evoked if the average efficiency target is not met. The Ministry of Economy Trade and Industry monitor the program and it is legislated through the Energy Conservation Law. The program so far has been quite successful, with most manufacturers gearing up to meet the targets. When the target year is reached, new target levels can be established.

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EER W/W Type Rated Cooling (CC) W

Cooling Only Heat Pump Window 2.67 2.85

CC < 3200 3.64 4.90 3200 < CC < 4000 3.08 3.65 4000 < CC < 7100 2.91 3.17 Split

CC > 7100 2.81 3.10 Table 4-4: JIS’s Room Air Conditioner MEPS

4.1.4 EER and Labeling in the U.S. Thailand and Korea The existing MEPS levels in the U.S. were introduced on 1 January 1990. More stringent levels were published in October 1997 and are due to take effect on October 1, 2000.

Other than minimum energy efficiency requirement, many countries introduced voluntary or mandatory labeling program to increase the awareness of the consumer as well as to allow the manufacturer charge a premium over more energy efficient products.

U.S. Thailand Korea

4.1.5 EER Comparison with China The chart below is from APEC AC Benchmarking Report prepared by the Danish Energy Management(12). The red line indicates current China EER requirement

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value at various size (capacity) units. The blue line represents Australia EER code level on 2004, dotted blue line is the improvement target on 2005 onward. To match those have higher EER code countries such as Thailand and Korea. Taiwan’s data is not shown, but should be in the range of Thailand pink line.

Chart 4-1: Current and proposed MEPS levels in five countries. The arrows indicate the development of MEPS in Australia over the next couple of years (in 2006 and 2007 respectively) expert from “EER in five APEC AC benchmarking report”, prepared by the Danish Energy Management, June 2004

Similar to Australia, China EER minimum requirement has room for improvement. The U.S. Department of Energy raised the unitary air conditioning equipment EER to 3.53 W/W (12 in Btu/h per Watt) from January 23, 2006. This is 20% improvement from the previous standard while it is still 10%~18% better than the 2009 China EER requirement. Currently in the U.S., the most efficient unitary AC unit has 4.7 W/W EER. This is 32% better than minimum requirement with tax incentive from U.S. I.R.S. (Internal Revenue Service).

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4.1.6 Weighted-average EER According to the market share of different brands and the corresponding EER of the product, Danish Energy Management(5) used the sales weighted average EER to compare the country’s overall energy efficiency ratio based on their different EER code requirement. The results as shown from the following chart indicated that China can use 21% less power supply for air conditioning if higher EER code similar to Korea is implemented.

Chart 4-2: Sales-weighted average EER in the five countries. Excerpt from “EER in five APEC AC benchmarking report”, prepared by the Danish Energy Management, June 2004

4.2. China Electrical Power Supply According to SGCC(13) (State Grid Corporation of China國家電網公司) estimation, by the year 2020, China power plant total installed capacity will reach one thousand

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GW. Current total capacity is around 400 GW, average increase on power plant installation will be around 33 GW per year for 15 years till 2020, or somewhere around 5.5% increase per year. Total capacity increase 2000~2003 is 6.88%, 6% and 8.39% respectively. Each year, China experienced different level of power shortage. The followings are data from SGCC.

Year 2001 2002 2003 2004 2005 2006Electrical Power

Supply Shortagesin GW

- 20.35 44.85 30 25 15~20

Table 4-5: Recent Year Electrical Power Supply Shortages in China

The shortage was due to: 1). An executive order to slow down the installation of new power plant after the 1997 Asia financial crisis; 2). Rapid economic growth after 2000.

4.3. RAC+PAC Electric Power Demand Incremental Changes Estimation Using 2009 forecasted figures, assuming 30 millions new unit to be installed in China. According to BSRIA survey, 90% will be RAC and the remaining 10% on PAC. As mentioned earlier in Chapter 2, the cost control model or average size of RAC is 3.5 kW (1 tons) and 17.58 kW (5 tons) for PAC. We can estimate the total electrical power load demand from newly installed air conditioner, using 33% utilization rate.

RAC 3.5 kW/unit x 30.163 M unit x 0.9 / 2.3 W/W = 41.310 GW,

PAC 17.58 kW/unit x 30.163 M unit x 0.1 / 2.4 W/W = 22.094 GW.

RAC+PAC total = (41.310 + 22.094) x 0.33 = 20.923 GW new installed capacity requirement.

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This is just a little over the installed capacity of the Three Gorges Dam hydropower plant 18.2 GW.

The results is similar to the total electrical power shortages experienced in the past 3~4 years as shown in Table 6. In other words, the main reason for the electrical power shortage came from air conditioner. The air conditioning load peak at the same time during summer, find way to effectively shed the peak load demand could help to offset the on peak and off peak difference and consequently build less new power plant. For example, 20% improvement on minimum EER requirement could means shaving peak load demand by 4 GW or equivalent to reduce 1.5 Lungmen (Nuclear) Power Plant No. 4 (1.35 GW per unit x 2) in Taiwan.

4.4. Estimating Total RAC+PAC Electric Power Demand at 2020 According to Professor Long, Wei Ding of Shanghai Tong ji University, by 2020, total number of air conditioner in China will be 420 million in operation. This is very similar to the estimation in Chapter 3.4 Using the same logics, 90% will be RAC and the remaining 10% on PAC. The average size of RAC is 3.5 kW (1 tons) and 17.58 kW (5 tons) for PAC.

RAC 3.5 kW/unit x 425.886 M unit x 0.9 / 2.3 W/W = 583.278 GW,

PAC 17.58 kW/unit x 425.886 M unit x 0.1 / 2.4 W/W = 311.961 GW.

RAC+PAC total = (583.278 + 311.961) x 0.33 = 295.429 GW installed capacity requirement.

The peak load demand estimation is similar to the estimation tabled below with a total of 331 GW installed capacity. Should China implement a new EER code with 20% improvement, there should have some 66 GW installed capacity reduction by

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2020. This is equal to 3.6 Three Gorge Hydro Power Plant or 36 normal size fossil fuel power plant.

Year

RAC + PAC Total(OD qty) -

Forecast(MultipleRegression)

AirConditionerSurvival rate

by 2020

Total OperationalAir Conditioner

by 2020

Demandfrom Newlyad Aircon in

GW

ReducedDemand

from HigherEER in GW

2006 21,114,672 x 0 0

2007 24,080,909 x 0 0

2008 27,122,156 x 0 0

2009 30,163,402 x 0 0 20.0

2010 33,204,649 x 0 0 22.0

2011 36,245,895 x 0.1 3,624,590 24.0 19.2

2012 39,287,142 x 0.4 15,714,857 26.0 20.8

2013 42,328,388 x 0.7 29,629,872 28.1 22.5

2014 45,369,635 x 0.9 40,832,671 30.1 24.1

2015 48,410,881 x 1 48,410,881 32.1 25.7

2016 51,452,127 x 1 51,452,127 34.1 27.3

2017 54,493,374 x 1 54,493,374 36.1 28.9

2018 57,534,620 x 1 57,534,620 38.1 30.5

2019 60,575,867 x 1 60,575,867 40.2 32.1

2020 63,617,113 x 1 63,617,113 42.2 33.7

2011~2020 tot. 499,315,042 425,885,972 331.1 264.9 Table 4-6: Total Air-con Peak Load Demand Estimation by 2020

4.5. Annual Consumption versus Peak Load Demand of Air Conditioners(14) Annual consumption of electrical energy by air conditioner is the total electrical power used in a year. Different from region to region, air conditioner operates 45~60 days a year in the north such as Beijing; 3~4 month in the east, such as Shanghai; a little bit longer in the south, such as Shenzhen. Annual consumption differs due to different operating hours in a year. They are not the same as the peak load demand. In 2001, it is estimated that 27.5% of the total energy consumed in China were for buildings while Japan is 29.2%. This includes both lighting and air conditioning, while air conditioning includes both cooling and heating. Total energy

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consumed includes electrical power as well as coal and other types of energy resources. While China total energy consumed for building is similar to Japan, this does not indicate buildings in China have similar comfort and safety level of developed country. 1).The air-con penetration rate remains low; 2). Air-con only operates a short period of time in summer (for example: 800~900 hours annual in Shanghai). However, 1).Air conditioner operates at poor efficiency; 2). Building has poor insulation and with air leak that does not conserve energy.

Demand for Air conditioning use is a major part of the supply by the electrical power company, due to people use air conditioner simultaneously in the summer. According to report, when the outside temperature is above 33°C, 1°C temperature rise, increased load by 127,000 kW in Shanghai in 2005, while Beijing are similar, at 32°C above, 1°C rise increased load 129,000 kW.

The following table indicates 2005 Beijing summer peak load is at 950,000 kW while 45% of the demand comes from air conditioning.

Year 2000 2001 2002 2004 2004 2005Summer PeakLoad ElectricalPower Supply

6.768 7.5 8.24 8.33 9.5 -

Percentage usedby air

conditioning36.94% 37% 40% 40% 45% -

Table 4-7: Beijing Summer Peak Electrical Load and ratio by Air Conditioner

Using SGCC estimation, 2020 China total installed capacity will be around 1,000 GW (900~1,100 GW, current around 400GW). Air conditioning demand at 331.1GW is about 30% of total supply during peak hours. Improving Air conditioner energy efficient is more economical than building new power plant only to meet the peak load.

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The following is a chart indicating approximately 40% of the peak load is for air conditioning use in a commercial building in developed market. While China is experiencing growing demand on electrical load, this chart provides information on peak load reduction strategy.

4.6 Life-cycle Cost effective EER Air Conditioner with high EER will cost more due to improved heat transfer efficiency. Higher EER may lower the operation cost, hence sell at higher price due to lower energy bill to the end user. A study by Jiang Lin and Gregory Rosenquist from the Lawrence Berkeley Lab(9) show that the optimal cost and benefit point is at 2.96 EER for 3500W split system heat pump, while the larger size 7100W optimized at 2.83 EER. The optimization point may varies from place to place due to different energy cost and material cost to build the high EER unit.

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C h a p t e r 5

CONCLUSION

China, already the largest producer of room air conditioner in the world since 2003, will move forward until it reaches its goal of building a harmonious society by 2020. According to our forecast, total number of RAC+PAC in operation will be around 426M units by 2020. This is a significant amount by any standard. Meeting the electrical power demand to operate these units is no easy task. It will be beneficial for both the economy as a whole and the consumers to use air conditioner with higher energy efficiency.

In a harmonious society, the standard of living improved, the middle class begin to form and grow. People stay in bigger and better house and work place, comfort environment become an essential. Air condition becomes a necessity, no longer extravagant. Effectiveness of the air conditioning system and energy efficiency is responsible use of the nature resource on earth.

5.1 Implementing new EER code by 2009 Implementing higher EER code for the Room Air Conditioner and Package Air Conditioner is the first step toward energy efficiency. The technology is readily available. There is a need to provide economical incentive to drive toward the goal. As the air conditioning market becomes more competitive, high energy efficiency is in fact a competitive advantage. From the consumer stand point, high EER means lower operating cost. Refer to paragraph 4.6, as the energy price keeps going up due to scarcity and the air conditioner selling price keep going down due to competition, the optimized EER could move further up to 3 or above.

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Government incentive is also an effective tool to encourage using high EER air conditioner. The U.S. Department of Energy implemented Tax credit incentive, effective January 1, 2006 on purchasing of energy-efficient cooling and heating products, as well as other energy conservation building material. A tax credit is generally more valuable than an equivalent tax deduction because a tax credit reduces tax dollar-for-dollar, while a deduction only removes a percentage of the tax that is owed.

EER improvement by 20% will help reduced peak load demand or new installed capacity by approximately 66 GW as calculated in the previous chapter. While 20% seems to be a big improvement, the 20% reduction from current EER requirement still 10%~15% behind the current code requirement of several advance country such as the U.S., Japan and Korea Besides, the technology advance may further improve the energy efficiency capability in the next 10 years.

As mentioned earlier, the life time of an air conditioner is around 7~10 years. Implementing the new EER on or before 2009 will allow most if not all of the RAC and PAC operating at the new standard on 2020.

5.2 Building Energy- Efficient Buildings China is now the world's fourth largest economy and the second largest energy consumer. Over 30% of the world's coal, steel and cement are now consumed in China. China's effort to improve the energy-efficiency of its buildings is important both for itself and the rest of the world. In the first half of 2006, China announced regulations on energy saving for civil buildings, standards for residential buildings and standards for technical evaluations of residential buildings. A new guideline, but not a compulsory standard, regarding the assessment of green buildings, asks for even higher standards in energy consumption and other environment-related indices, took effect on June 1, 2006.

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Building energy consumption accounts for 30% of the country's total energy consumption. It increases to 40% if energy consumption for manufacture and transportation of construction materials is considered. According to Jiang Yi, professor from the Tsinghua University, if no improvement is made, building energy consumption in China will double by 2020.

The application of new materials with better insulation and energy-efficient equipment is a major part of the effort to reduce building energy consumption. China like any other government in the world is the largest building owner, therefore must begin to renovate its existing buildings to become more energy-efficient. Policies that will provide tax rebates and other financial incentives for the construction and purchase of energy-efficient buildings are essential when it comes to private buildings and house owner.

5.3 Diversified Energy Source(15) Central System air conditioning has the benefit of using alternative energy source. Gas-engine driven chiller or direct-fired absorber can provide chilled water to cool buildings without using electrical power. Ice storage system can store ice during off-peak hours and provide peak time cooling from melted ice. DHC (district heating and cooling) is also a way to take advantage of the cooling/heating load diversification to reduce peal load demand for large commercial building complex and for institution such as university campus.

As for residential and light commercial application, electrical driven air conditioner seems to be inevitable. However, just as the US must lead the way in reducing oil usage (since the country uses one quarter of the world's oil each year), China must work hard to reduce coal usage if it is to expect a stable climate in the future. In 2004, China was already the largest user of coal. China and India together now use 42% of the world's coal - the fossil fuel with the greatest impact on human health

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and the climate. With such a large portion of China's population living on the coasts, it is essential that China takes an aggressive position on addressing climate change.

So far China has put forward the world's most ambitious plans for nuclear reactors, expecting to add 31 reactors by 2020 to nine operating ones and two under construction. This will mean opening two new large reactors each year.

In 2004, China became one of the largest producers of hydro-electricity, making up 11.7% of the world total (just 0.3% less than Canada), a significant increase over the past decade. By 2005, China took the place as the largest producer when the Three Gorges Hydro Power starts running.

In the field of wind power, China has the wind resources and manufacturing skills to become the world leader. It has a total approaching 1,270 megawatts so far. With the new Renewable Energy Law going into force since January 2006, Chinese and international companies are positioned to increase wind turbine output.

In 2005, the global production of photovoltaic (PV) cells - which generate electricity directly from sunlight - increased 45% to nearly 1,730 megawatts, six times the level in 2000. Although Japan, Germany and the United States lead the world in PV production, China accounted for almost 42% of the total production by the rest of the world. In late 2005, Shanghai launched an initiative to install PV systems on 100,000 of the city's 6 million rooftops.

5.4 Green GDP According to the estimates of the World Bank, environmental pollution and ecological damage took 15% away from the world's total GDP in 2003 in the scenario that year's total GDP grew by 10 per cent. In other words, 15 cents had to be deducted from each dollar earned. The above 9% on average growth in the past

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10 years may not seems as good when taking into account of the pollution and damage of environment in China.

This kind of overdrawing on resources does not happen in the cyclic economy due to the important principle of "generational fairness," meaning nobody has the right to make his or her own gains at the cost of the future. China needs to take urgent measures within the coming 10 to 20 years, to address climate change and prevent the worst of its effects. As the country is in a continuous growing stage (not cyclical), it could prioritize growth in certain sectors over others such as solar energy, wind energy, bio-fuels and ecologically friendly manufacturing.

The world's gross world product (GWP) - the sum of all finished goods and services produced globally - jumped 4.6% in 2005 to a record high of US$59.6 trillion (in 2005 dollars). China produced 14% of the world's GWP in 2005 and despite the high proportion, the index grow 7.8% year-on-year. With such huge growth and 1.3 billion people, all changes regarding China are significant. Although, per capita usage in China is small compared to industrial countries, but its huge population makes its trends significant, be it food, natural resources or energy.

5.5 Sustainable Growth China must find a way to build a sustainable economic system now. Instead of building a Western-style infrastructure that was design when resource and energy were abundant, grow along with the available resource and limited environmental damage is the only viable way.

The rapid growth of the Chinese economy over the last 15 years has been driven by low-cost labor and huge capital, resource and energy input. The cost will rise and the advantages will gradually fade away if it continues along this line of development.

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The sustainable growth-orientated concept will bring about changes in the Chinese economy, and society. And concept will show effects by 2020 when the goal of bringing about a harmonious society will have been achieved and China's economic strength will be significantly greater than today.

End of NCCU EMBA Program Thesis by You-ching Jen “Eugene”

china room air conditioner & package air conditioner … .pdf · 2008. 1. 9. · china room air...

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