kalimantan tengah green economy model (kt-gem) · kalimantan tengah green economy model (kt-gem)...

Kalimantan TengahGreen Economy Model(KT-GEM)

Pavan Sukhdev, Kaavya Varma, Andrea Bassi and Sonny Mumbunan

LECB INDONESIAFINAL REPORT

Kalteng’s Green Economy Model (KT-GEM)Final Report

Pavan Sukhdev

Kaavya Varma

Andrea M. Bassi

Sonny Mumbunan

LECB Indonesia Final Report

LECB Indonesia Final Report

© 2015 Low Emission Capacity Building (LECB)

All rights reserved

Suggested citation:

Sukhdev,P., Varma,K., Bassi, A.M., and Mumbunan, S. 2015. Kalimantan Tengah Green Economy Model (KT-GEM). Low Emission

Capacity Building Programme, Indonesia.

Cover photo credit: S. Mumbunan.

UNDP Indonesia

Menara Thamrin 8-9th floor

Jl. M.H. Thamrin Kav. 3

Jakarta 10250

This Final report is intended to communicate initial findings or methods used in LECB Programme in Indonesia to promote further

policy discussion on Green Economy. Any view expressed in this report does not necessarily represent the views of the institutions

or the sponsors of this publication.

Abbreviations iiExecutive Summary 11. I-GEM and its applicability for Provinces 22. Green Economy Indicators for Kalteng 33. Kalteng Green Economy Model (KT-GEM) 44. Results- Business as Usual 6- Green Economy Scenarios 227. Policy Implications based on Scenario Analysis 358. Value Addition for BAPPEDA 36References 42

Table of Contents

Acknowledgement

We thank the following persons for their valuable cooperation and insightful inputs for the development of the Kalimantan Tengah

Green Economy Model and this report;

The Governor and Vice Governor of Kalimantan Tengah, Head of BAPPEDA and Assistant to the Governor on Economic and De-

velopment of Kalimantan Tengah.

The provincial officials; Warismun, Domingus Neves, Akhmad Elfiansah, Retno Nurhayati Utaminingsih, Jani Dwipriambodo, Fir-

manto, Indah Susanti Rosga, Edwin Adi Pratama.

The University of Palangkaraya; Yusurum Jagau, Jhon Wardi, Tri Yuliana.

Dr. Medrilzam (BAPPENAS), Dr. Muh.Tasrif, Akhmad Taufi, Hani Irwan (Bandung Institute of Technology), Puspa K. Wijayanti and

Verania Andria (UNDP) and Johan Kieft (UNORCID).

iKalteng Green Economy Model

Figure 1: Total population and labour force 6

Figure 2: Real GDP (for industry and services, and agriculture) and its growth rate 7

Figure 3: Tourist arrivals and sectoral GDP 9

Figure 4: Government revenue and expenditure, and total capital formation (public and private) 10

Figure 5: Agriculture and settlement land 11

Figure 6: GDP from crops and livestock and forest cover 12

Figure 7: Fisheries and mining GDP 13

Figure 8: Electricity demand and energy bill over GDP 15

Figure 9: Road network and vehicle stock 16

Figure 10: KT-GEM BAU simulations for Green Jobs in Kalteng 19

Figure 11: KT-GEM BAU versus GE simulation 21

Figure 12: Productivity per hectare and crops and livestock GDP: Scenario 1 23

Figure 13: Forest cover and annual emissions from forest: Scenario 1 24

Figure 14: GDP of the Poor and per capita real disposable income 25

Figure 15: Food balance and agriculture land: Scenario 2 26




Figure 19: Simulation results, Index (2000 = 1) for GDP of the Poor and per capita

real disposable income: Scenario 3 30




Figure 23: Simulation results, Index (2000 = 1) for GDP of the Poor and per capita

real disposable income: Scenario 4 34

Table 1: List of assumptions and policies that can be simulated with KT-GEM 4

Text box 1: GDP of the Poor 17

Text box 2: Decent Green Jobs 18

Text box 3: Green GDP 20

Text box 4: Caveats and recommendations for model use 37

Text box 5: The development of KT-GEM 39

List of Figures

List of Tables

List of Textboxes

ii LECB Indonesia Final Report

List of AbbreviationsI-GEM :Indonesia’s Green Economy Model

BAPPEDA :Badan Perencana Pembangunan Daerah (Regional body for Planning and Development)

BAU :Business as Usual

GDP :Gross Domestic Product

GE :Green Economy

ILO :International Labour Organisation

JAK-GEM :Jakarta Green Economy Model

KT-GEM :Kalteng Green Economy Model

LECB :Low Emissions Capacity Building

MW :Mega Watt

1Kalteng Green Economy Model

Transitions towards a ‘Green Economy’ are being sought actively by many nations, and Indonesia is a

leader among them. “I-GEM” (Indonesia Green Economy Model) is a flexible and easy-to-learn na-

tional level System Dynamics Model that has been developed as part of a capacity building programme

of United Nations Development Programme (UNDP) with support from the United Nations Environ-

ment Programme (UNEP) and in collaboration with the United Nation’s Office for REDD+ Coordina-

tion in Indonesia (UNORCID), to evaluate trade-offs and test the sustainability dimensions of policy

interventions in provincial economy., to evaluate trade-offs and test the sustainability dimensions of

policy interventions in provincial economies. The first such provincial level application of I-GEM has

been undertaken for Central Kalimantan Province of so called Kalimantan Tengah Green Economy

Model (KT-GEM), which is tailored to incorporate an additional set of three ‘Green Economy’ out-

come indicators. The implementation of this type of model at a provincial level has significant value

added for provincial officials who are seeking to assess the impacts of policy interventions that they are

planning, to increase employment opportunities, reduce rural poverty and ensure economic growth in

the long-term by maintaining their administrative zone’s natural capital.

Executive Summary

2 LECB Indonesia Final Report

1. I-GEM and its applicability for Provinces

I-GEM is a national level “demo” model that has specifically

been designed to support policy formulation and evaluation,

for a variety of goals, including green economy and sustain-

able development in Indonesia. I-GEM seeks to provide policy-

makers with the ability to compare how policy interventions

that they make under business as usual and green economy

scenarios can have differing impacts on rural poverty, green

GDP and on jobs. Policy-makers are able to view these impacts

over horizons (1 year, 5 years and 10 years), so that they are able

to assess whether they want to go ahead with a particular policy

intervention or not and, thereby, make more informed deci-

sions. I-GEM can assess impacts on several variables (depend-

ing on what the policy-maker inputs into the model), however,

three critical “outcome indicators” such as “GDP of the Rural

Poor”, “Green GDP” and “Decent Green Jobs” are utilized by

the model to display what the effect of selected or intended

policies is on the incomes of the poor, the green GDP and the

availability of decent green jobs in a province.

Such an assessment undertaken at a provincial level empowers

planners and other local government officials to decide how to

chart a path towards a green economy within their provinces,

as I-GEM is able to demonstrate whether the policies they se-

lect will indeed result in decent employment generation, re-

duce poverty and lessen the degradation of natural resources

in their administrative zones – all criteria for achieving a green

economy. A pilot implementation of I-GEM has been conduct-

ed in Kalteng, referred to as KT-GEM that demonstrates how

local officials in Kalteng can utilise the results that KT-GEM

is presenting to shift towards a green economy. The sections

below briefly describe the three indicators and what they mean,

outline the findings of KT-GEM and discuss how policy-mak-

ers can use these findings to move towards a green economy in

Kalteng.

Moreover, if Indonesian provinces utilize such a model as a

complementary addition to their existing policy implementa-

tion processes, it would facilitate the national achievement of a

green economy for Indonesia. Keeping this in mind, this report

also recommends the development of context specific models

for urban contexts (see supplementary report on the develop-

ment of JAK-GEM for Jakarta), so that Indonesia has repre-

sentative models for diverse economies and landscapes. Finally,

the report ends with a summary of the KT-GEM model param-

eters and lists the agencies engaged in the stakeholder consul-

tations to allow other provinces to be informed about who the

key stakeholders should be when they are developing their own

provincial models. This summary in Text Box 1 can be treated

as a separate insert for quick reference on model construction.


2. Green Economy Indicators for KaltengShifting towards a green economy requires the capacity for a

province like Kalteng to identify what its natural asset base is,

what the dependence of its population (particularly the rural

poor) is on this asset base and where the potential lies for cre-

ation of jobs that promote these critical inter-linkages, so that

they result in sustainable economic growth in Kalteng. I-GEM

utilises three indicators, “Green GDP”, “GDP of the Poor” and

“Decent Green Jobs”, which can provide policy-makers with a

concise set of indicators that capture and appropriately mea-

The graphical simulations in the following sections demon-

strate the impacts in Kalteng on the economy, environment and

equity under business as usual (BAU) versus green economy

(GE) scenarios. This report focuses on providing detailed im-

pacts going down to the household level in Kalteng and dis-

cusses BAU versus GE scenarios for selected economic sectors

that have been validated at a provincial level to ensure that key

sure the value of benefits provided by natural resources at the

district level and how sectors based on these natural resourc-

es can be targeted to generate employment opportunities for

communities. Economic estimations utilizing these indicators

can be utilized by planning and environment officials to estab-

lish zones of development that stimulate clusters of innovation

to emerge in Kalteng. These interventions would be more com-

petitive because they would reflect the social and environmen-

tal needs, realities and capacities of Kalteng.

issues identified by Kalteng’s policy-makers have been taken

into account. Table 1 provides a list of the assumptions and

policies that reflect the requirements identified by Kalteng of-

ficials. Specific information on the ways in which KT-GEM

further fits into the national green economy transition for In-

donesia can be found in the report “Indonesia Green Economy

Model (I-GEM)”.

DecentGreenJobs

GDP ofthe Poor

GreenGDP

Measures the value of household incomes of rural and forest dependent communities including economically invisible - but critical and valuable - ecosystem services

Captures and estimates the invisible economic benefits from ecosystem services, and accounts for depreciation of natural capital (i.e. degradation and depletion of ecosystems and their services over time). Green GDP also includes account-ing for changes in the value of Human Capital (education, skills, health)

Is the direct employment created in different sectors of the economy and through related activities that reduces the environmental impact of those sectors and activities, and ultimately brings it down to sustainable levels. Jobs also have to meet the “decency” criteria where they empower employees (ILO)


3. Kalteng Green Economy Model (KT-GEM)

Table 1: List of assumptions and policies that can be simulated with KT-GEM

Assumptions Policies

Population

TransmigrationBirth Control Policy

GDP

Maximum Life ExpectancyTech Rate Of Change

Goverment

Domestic Revenue Aggregate Tax Rate Time SeriesNon Tax Revenue Share Of GDP

Households

Investment Share

Land Use

Food Per PersonTarget Year For Agriculture Conversion StopMaximum Forest ConversionAvoided Deforestation Policy Switch

Agriculture

Conventional Food Crop To Plantation Reduction Due To Income ExpectationsOrganic Agriculture Investment Per Ha

Degraded Forest Conversion To PlantationSustainable Agriculture Target HaAgriculture Target YearConventional Agriculture ProductivitySustainable Agriculture ProductivityShifting Food Crop Productivity Table

Fishery

Fish Conservation PolicyFish Conservation Policy SwitchVessel Removal PolicyVessel Removal Policy Switch

Mining

Land Use Per Million Metric Ton Mining Resources Fraction Recoverable


Assumptions Policies

Energy

Effect Of Improved Electrification On Electricity Demand Per PersonElectricity Shortage Switch

Energy Efficiency Policy SwitchEnergy EfficiencyHydro Energy Policy SwitchHydro Construction Start YearDesired Hydro CapacitySolar Energy Policy SwitchSolar Construction Start YearDesired Solar Capacity

Tourism

Desired Tourism Arrivals Growth RateGDP Effect On Tourism Arrivals

Roads

Elasticity Of Road Accessibility Target To Population Density

Forest Cover

Conversion Primary To Plant ForestEffect Primary ForestSecond Forest ConversionEffect Second ForestPrimary Forest LoggingLogging Second ForestProductivity Of Plant Forest

Conservation Forest

Conversion Of Conservation ForestPercentage Of Other Utilization

Convertible Forest

Effect Conversion ForestTimber Produced By Logging

Forestry Economics

CO2 Price Per TonValue Added Forestry Table


4. Business as UsualPopulation dynamics are endogenously simulated in KT-GEM,

and strongly influenced by migration flows. In turn, migration

is mainly driven by employment opportunities. In the BAU sce-

nario, population is projected to increase reaching 3.53 million

people in 2030 (Figure 1)

In particular, GDP is also projected to grow, primarily due to

the further development of industry and services, including the

tourism sector. As shown in Figure 2 and, real GDP is expected

to grow from Rp 25,43 billion in 2015 to Rp 51,78 billion in

2030.

Figure 1: Simulation results, total population and labour force


Figure 2: Simulation results, real GDP (for industry and services, and agriculture) and its growth rate


Figure 3: Simulation results, tourist arrivals and sectoral GDP

GDP growth in Kalteng is driven primarily by (i) investment,

(ii) employment, (iii) productivity and (iv) availability of natu-

ral resources. For Kalteng officials it is important to increase

GDP growth as a conventional measure of development for the

province and therefore, identifying those sectors which directly

contribute to its increase is significant.

Figure 4 presents government revenues and expenditures at the

national level, as well as total investments (gross capital for-

mation, public and private). Data inconsistencies were found

across indicators, as shown by the results of government ex-

penditure and capital formation. From an initial review of the

data, it seems evident that public revenue data underestimate

the actual receipts of the government, and this has implication

on expenditures (also underestimated in the data).


Figure 4: Simulation results, government revenue and expenditure, and total capital formation (public and private)


Figure 5: Simulation results, agriculture and settlement land

While government expenditures along with revenue and grants

clearly increase under BAU, based on the data available, it is

important to consider which sectors are receiving most of

the fiscal support. This is because investments in secondary

and tertiary sectors are likely to lead to sustainable growth in

Kalteng and officials then need to further simulate a detailed

breakdown of the expenditures and grants to determine if ac-

tivities within these sectors are adequately being targeted in

budgets.

Furthermore, the main sectors in Kalteng that contribute to

GDP growth such as tourism, agriculture and industries and

services are considerably dependent on natural resources as

their fundamental asset base. Therefore, it is necessary to see

what happens to this natural capital under a business as usual

scenario.

The following graphs present projections for the use of natural

capital, and resulting value added creation. First, land use is

presented for agriculture and settlement land (Figure 5). It can

be noted that a growing population is projected to lead to an in-

crease in both types of land, impacting the trends of forest and

fallow land as well (Figure 6). It can also be noted that while

increasing, value added from agriculture and fisheries is not

projected to grow as fast as industry (e.g. mining) and services

(e.g. tourism). Mining production (presented in physical and

monetary terms), which in the model includes the endogenous

simulation of undiscovered resources and discovered reserves

as well as resulting land clearing from production and possible

impacts on water pollution, is presented in Figure 7.


Figure 6: Simulation results, GDP from crops and livestock and forest cover


Figure 7: Simulation results, fisheries and mining GDP


From a policy perspective it is clearly evident that Kalteng is

going to see a rise in expansion of agriculture and settlement

land due to population growth in the province. It is important

for the government of Kalteng to see where the land is going

to come from for this expansion, as business as usual trends

also show a decrease in forest area. This could be a result of the

fact that forest area is being cleared to make space for habita-

tion and cultivation. Such a correlation would spell significant

negative impacts for the natural capital of Kalteng, so the need

for additional yields and space should be addressed through

improvements in crop prices in the markets, effective distribu-

tion to avoid spoilage, better storage facilities for farmers to en-

sure they can access the best prices, recognition of smallholder

farmers and overall establishment of zoning plans that allow

for increased density creation in terms of housing rather than

further horizontal urban expansion in Kalteng.

In addition, existing policies need to be reviewed to assess

where mining licenses are being provided to identify which

areas would be opened for mining in the next twenty years.

Kalteng is unique in its rich natural capital and biodiversity and

could provide a model for other provincial governments on

how to generate economic growth, social justice and poverty

alleviation by understanding the value of natural capital and

managing it sustainably.

Infrastructure is also key to enabling inclusive economic

growth and social development. Electricity demand and supply

as well as the road networks are included in the current version

of KT-GEM. Population and GDP, electricity prices and energy

efficiency drive electricity demand. Supply instead is driven by

investments (also affected by electricity demand), which allow

the construction of power generation capacity (measured in

MW). Both demand and supply are projected to grow in the

BAU scenario, with thermal generation dominating power

generation, leading to an increasing energy bill going forward

(Figure 8). In particular, thermal capacity would increase sig-

nificantly between 2015 and 2030, while no investments are ex-

pected in renewables and hydro capacity expansion.


Roads are also affected by population growth and GDP (or in-

come). KT-GEM projects an increase in the vehicle stock, lead-

ing to increased congestion (e.g. especially in Palangka Raya)

and the subsequent expansion of the road network (either in

length or capacity), also due to a required increase in acces-

sibility (Figure 9).

While these trends will have several impacts (e.g. increased

road maintenance costs, higher energy consumption and emis-

sions, as well as potentially a higher number of accidents), these

are not currently included in the model, but may be added later

based on data availability.

Figure 8: Simulation results, electricity demand and energy bill over GDP


Figure 9: Simulation results, road network and vehicle stock


The GDP of the Poor indicator measures the contribution

of nature to household incomes of rural and forest-depen-

dent communities. These incomes and dependencies on

nature are usually economically invisible - but critical and

valuable - and capturing them through KT-GEM can di-

rect policies towards these significant sectors that contrib-

ute to ensuring the well-being of poor people in Kalteng.

The initial survey conducted in Kalteng to determine the

GDP of the Poor showed that households with no alter-

native sources of income to the forest and riverside eco-

systems in which they live are overwhelmingly dependent

upon those ecosystems (see Table 1). As expected, house-

holds involved in rattan and coal production - who have

distanced themselves from natural ecosystems and adopt-

ed mixed productive economies - are less directly depen-

dent upon ecosystem services.

GDP of the Poor

Ecosystem Services Dependence in Central Kalimantan

Type of VillageTotal average ecosystem based Non Cash Income

(% of total income)

Total average ecosystem based Cash and Non Cash

Income (% of total income)

Forest

N = 31 households (Murung Raya District)51.43 77.41

Riverside

N = 44 households (North Barito, South Barito, Pulang Pisau and Kapuas Districts)

43.55 86.38

Rural mixed with rattan

N = 27 households (Katingan District)44.63 74.99

Rural mixed with coal

N = 22 households (North Barito and South Barito) 21.79 34.14

All type

N = 119 households43.63 76.38


K-GEM further shows that under a business as usual sce-

nario, households that are dependent on natural resourc-

es (rattan, forest and riverside) are not projected to have

improvements in terms of their incomes for the next ap-

proximately twenty years. This has several implications

from a policy perspective; a) natural resources that these

households are dependent upon are getting degraded and

therefore, not receiving adequate and appropriate sustain-

able management, b) fish stocks are getting depleted which

would have adverse impacts on supply of such nutrition

beyond just the health of these households to other areas

where fish are being sold, c) employment and livelihood

opportunities will have to be created to make up for the in-

comes lost due to the degradation of natural resources and

local officials will need to address this need, d) migrations

to urban areas could increase placing these areas under

further stress and e) the capacity of these households to re-

cover from adverse climate impacts would weaken making

it important for Kalteng officials to invest resources that

will only have short-term beneficial impacts because they

will not be addressing the fundamental problem.

In order to measure the impact of policy interventions

on the nature and number of new jobs created or old jobs

lost due to a green economic transition, a second indica-

tor is needed: ‘Decent Green Jobs’. Decent Green Jobs are

defined by the International Labour Organisation (ILO)

as direct employment created in different sectors of the

economy and through related activities that reduces the

environmental impact of those sectors and activities, and

ultimately brings it down to sustainable levels. A KT-GEM

analysis, utilising data collected by ILO, at the provincial

level shows the following trends in Decent Green Jobs in

Kalteng.

Kalteng has a greater proportion of jobs that could be con-

sidered to be both “green” and “decent” than the national

level, with green jobs estimated to be linked to 9 percent

of jobs in the province in 2010. The majority of green jobs

within the province are found in the agriculture, forestry,

hunting and fishery sectors. Employment is growing in

both palm oil and in rubber, and it is important to promote

more environmentally friendly models for these industries,

such as “jungle rubber”, “rubber inter-cropping” to reduce

the environmental impact of these sectors. Employment in

the construction industry has been increasing, particularly

in building construction, and it is important to promote

alternative materials, technologies and low impact work

practices, as well as environmental compliance, to reduce

the environmental impact of this sector.

Jobs in solid waste management and in management

of tourism destinations, such as national parks, have in-

creased and there are signs of job quality improvement in

Decent Green Jobs


this sector as well. Indeed, all jobs in the management of

gardens, national parks and agro-tourism were considered

to meet the criteria for decent work. Ecotourism accom-

modation and related services are still very limited in Cen-

tral Kalimantan, and an area for potential growth.

Such an analysis is extremely important for local officials

who are responsible for creating development in Kalteng

and who often find it difficult to contextualise environ-

mental preservation within jobs creation and revenue

generation. The analysis based on this indicator would not

only allow them to increase investments in jobs that are

sustainable and based on regional capacities, but also those

that are socially defensible.

KT-GEM in addition, shows through simulations in a BAU

scenario that there is clear value that the tourism and the

fisheries sectors are providing for Kalteng in the form of

employment opportunities (which is where green jobs are

projected to increase). For policy-makers it would be pru-

dent to invest further in these sectors to encourage growth

that is taking place because it is sustainable. Moreover, it

is important for policy-makers in Kalteng to protect these

sectors since they are visibly demonstrating economic val-

ue for the province.

Figure 10: KT-GEM BAU simulations for Forest Area and Green Jobs in Kalteng


KT-GEM also shows that jobs in the agricultural and for-

estry sectors decline over the next approximately thirty

years. Thus, under business as usual these sectors are not

being utilised sufficiently to create employment opportu-

nities in Kalteng. Additionally, the decline in employment

opportunities in these sectors could also indicate a degra-

dation in related ecosystem services, which could be why

the number of people working in them are declining.

As a whole, under a GE scenario in KT-GEM the number

of jobs in green sectors is higher, which means that Kalteng

can explore of how much of its needs for growth and rev-

enue generation can be met through these additional jobs.

The beneficial impacts are seen in rural household incomes

as well under GE, which means that poverty alleviation

policies in Kalteng should promote the recognition of the

contribution of nature to rural livelihoods if they are to be

effective in improving well-being and meeting internation-

al development targets.

Green GDP was calculated for Kalteng, which estimates in-

visible economic benefits from ecosystem services, and ac-

counts for depreciation of natural capital (i.e. degradation

and depletion of ecosystems and their services over time).

To develop Green Accounts, data on forests, agriculture,

freshwater and human capital was needed. More specifi-

cally, the following data was collected in Kalteng.

Forests

Changes due to economic activities

Timber, Fuelwood, Non-timber Forest Products &

Carbon

Soil Conservation, Water Augmentation & Flood

Prevention

Ecosystem and Species Diversity Values

Bio-prospecting Values (if relevant)

Existence Value of Biodiversity

Agricultural Cropland & Pasture Land

Freshwater

Subsoil assets

Human Capital – Education & Health

Overall, it was found that Green GDP was consistently

higher as compared to GDP as Figure x below shows in

terms of the difference between the two. This means that

Kalteng will benefit from GE interventions down to the

level of household incomes, particularly in five and twenty

years.

Green GDP


Figure 11: KT-GEM BAU versus GE simulation


5. Green Economy ScenariosSeveral scenarios can be simulated with KT-GEM, as presented

in Table 1. A specific GE scenario was developed and tested

during the validation workshop that emphasizes land use op-

tions. These include:

(Scenario 1) an improvement of agriculture productiv-

ity (both on food crop shifting land and on conventional

land),

(Scenario 2) the elimination of conversion from forest

to agriculture land,

(Scenario 3) the reduction of deforestation (reaching

a maximum of 2.5 million ha cumulatively from 2012)

and

(Scenario 4) the reduction in the expansion of planta-

tions (e.g. palm oil). These four scenarios were devel-

oped specifically based on inputs from Kalteng officials

and other key stakeholders (e.g. from Palangkaraya

University) during the consultations.

Other interventions address all sectors, emphasizing – in the

current version of the model - the agriculture, fishery, forestry,

mining and energy sectors. Interventions include:

The promotion of sustainable agriculture,

The reduction of vessels and boats as well as fish stock

management interventions to support maintenance and

replenishment of the stock,

Reforestation (in the same amount as deforestation of

primary and secondary forest) to curb the reduction of

forestland, and

The improvement of energy efficiency (for electricity)

and the expansion of the use of renewable energy. Ad-

ditional policies and assumptions include the mining

and tourism sector, as well as population (demographic

development) and the economy (e.g. investments and

technological improvement).

The impacts on the environment, economy and incomes of

Kalteng in each of the four scenarios can help policy makers

in Kalteng decide which policy interventions they would like

to implement in their province depending on the desired out-

comes.

Scenario 1: The first scenario simulated assumes an increase in agricultural pro-ductivityThis is done to increase food production and self-sufficiency

as well as to reduce the pressure on forest cover. Specifically, it

is assumed that conventional land will see a 33% improvement

in productivity, and shifting food crop land will reach a 50%

increase in productivity. The results are presented in Figure12,

Figure 13 and Figure 14.


Figure 12: Simulation results, productivity per hectare and crops and livestock GDP: Scenario 1


From the results of this scenario we can note that the average

yield per hectare is projected to increase, leading to higher

production and GDP for the agriculture sector. With higher

food production the food balance is projected to stabilize after

2020, and with higher yield agriculture land declines, reducing

pressure on deforestation and hence allowing forest cover to

be larger than in the Business As Usual scenario. With higher

forest cover the amount of emissions originating from land use

is also projected to decline as Figure 13 shows.

Figure 13: Simulation results, forest cover and annual emissions from forest: Scenario 1


When considering the comparison of incomes it can be noted that there are marginal gains, (Figure 14) both for the poor (dueto the larger forest cover and agriculture

land productivity) and for households more in general (because of the higher value of agriculture production).

Figure 14: Simulation results for GDP of the Poor and per capita real disposable income: Scenario 1

Scenario 2: Increase in agricultural pro-ductivity and the elimination of agricul-tural land conversion to settlementsThe second scenario simulated assumes, in addition to the

increase in agricultural productivity, the elimination of agri-

cultural land conversion to settlements. This is done to reduce

the impact of population growth on agriculture production,

as well as to reduce the pressure on forest cover. Specifically,

it is assumed that no more agriculture land will be converted

to settlements from the year 2020. The results are presented in

Figure 15.

Given the small flow of land projected to be converted from

agriculture to settlements going forward, the results of this

scenario do not show meaningful differences when compared

with Scenario 1.


Figure 15: Simulation results, food balance and agriculture land: Scenario 2



Scenario 3: In addition to what is tested in Scenarios 1 and 2, Scenario 3 assumes a complete stop of deforestation when the cumulative amount of 2.5 million hectare of forest has been converted, starting from 2012

In scenario 3 the complete stop of deforestation is projected

from 2012 to protect forest areas, by maintaining forest cover

at 67% of total land in Central Kalimantan. The results are pre-

sented in Figure 16, Figure 17 and Figure 18.


From the results of this scenario we can note that the thresh-

old of 2.5 million hectares of forestland conversion is reached

in 2023. After this time, the conversion from forest to agricul-

ture reaches zero (i.e. the policy is enforced), and agriculture

land declines below BAU. Specifically, this is shifting food crop

land, which also sees a decline in total production and food

balance. On the other hand, forest cover increases (it becomes

constant from 2023) and emissions from forests decline to

zero.

When considering the comparison of income, in Figure 19, it

can be noted that there are marginal gains for the poor (due to

the larger forest cover and agriculture land productivity) but

a decline for other households (because of the lower value of

agriculture production).

Figure 19: Simulation results, Index (2000 = 1) for GDP of the Poor and per capita real disposable income: Scenario 3

Scenario 4: In addition to what is tested previously, Scenario 4 assumes a reduction in the expansion of plantations (e.g. palm oil)This is done to increase food production and self-sufficiency,

countering the constraint created by the avoided deforestation

policy. Specifically, it is assumed that plantations will still grow

(reaching 2.3 million ha by 2020 and remaining constant until

2030), but will remain 30% below BAU. The results are present-

ed in Figure 20, Figure 21, Figure 22 and Figure 23.



Average Yield Per Ha2

1.5

1

0.5

0

5 5 5 5 5 5 5 54 4 4 4 4 4

44

3 3 3 3 3 33

33

2 2 2 2 2 22

2

2

1 1 1 1 1 1 11

1

2000 2004 2008 2012 2016 2020 2024 2028Time (Year)

Ton/

Ha

Average Yield Per Ha : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1 1Average Yield Per Ha : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2 2Average Yield Per Ha : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3 3Average Yield Per Ha : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4Average Yield Per Ha : BAU 18 Dec LU1 BAU 5 5 5 5 5 5

crops and livestock gdp8e+12

6e+12

4e+12

2e+12

0

66

6

5

5 5 5 55 5

5

4

4 4 4 44

44

3

33 3 3

33

3

2

22 2 2

22

2

1

1

1 1 1 11

11

2000 2004 2008 2012 2016 2020 2024 2028Time (Year)

Rp2

000/

Year

crops and livestock gdp : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1crops and livestock gdp : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2 2crops and livestock gdp : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3crops and livestock gdp : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4crops and livestock gdp : BAU 18 Dec LU1 BAU 5 5 5 5 5 5crops and livestock gdp : Kalteng data v2 6 6 6 6 6 6



Agriculture Land6 M

4.5 M

3 M

1.5 M

0

65 5 55

55

55

4 44

44

44 4

3 3 33

33

33

2 22

22

2 2 2

1 11

11

1 1 1 1

2000 2004 2008 2012 2016 2020 2024 2028Time (Year)

Ha

Agriculture Land : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1 1Agriculture Land : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2 2Agriculture Land : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3 3Agriculture Land : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4 4Agriculture Land : BAU 18 Dec LU1 BAU 5 5 5 5 5 5 5Agriculture Land : Kalteng data v2 6 6 6 6 6 6



Forest Area10 M

8.5 M

7 M

5.5 M

4 M

6 6

6

55

55

55

55

4 44

44

44

4

3 33

33

33

3

2 22

22

22 2

1 11

11

1 1 1 1

2000 2004 2008 2012 2016 2020 2024 2028Time (Year)

Ha

Forest Area : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1 1Forest Area : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2 2 2Forest Area : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3 3Forest Area : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4 4Forest Area : BAU 18 Dec LU1 BAU 5 5 5 5 5 5 5Forest Area : Kalteng data v2 6 6 6 6 6 6 6

annual co2 emissions from forest40 M

20 M

0

-20 M

-40 M

66

5

5 5 5 5 5 5 5

4

44 4 4

4 4 4

3

33 3 3

3 3 3

2

22

2 22 2

2

1

1

1 1 1 1

1 1 1

2000 2004 2008 2012 2016 2020 2024 2028Time (Year)

Tons

/Yea

r

annual co2 emissions from forest : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1annual co2 emissions from forest : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2annual co2 emissions from forest : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3annual co2 emissions from forest : BAU 18 Dec LU1 + Pr 4 4 4 4 4annual co2 emissions from forest : BAU 18 Dec LU1 BAU 5 5 5 5 5 5annual co2 emissions from forest : Kalteng data v2 6 6 6 6 6


From the results of this scenario we can note that the reduced

expansion of plantations more than offsets the impact of the

avoided deforestation policy (Scenario 3). In fact, agriculture

production, GDP and the food balance all reach their high-

est values (i.e. same as in the BAU case or Scenario 1). On

the other hand, forest cover is at its highest level as well, and

emissions from forests reach zero from the year 2020. This

indicates that the slower growth in plantations simulated in

Scenario 4 does not create tradeoffs between agriculture and

forestry, instead, it leads to multiple benefits (although GDP

and employment from plantation will be below the BAU case).

When considering the comparison of income, in Figure 23,

it can be noted that there are again marginal gains, both for

the poor (due to the larger forest cover and agriculture land

productivity) and for households more in general (because

of the higher value of agriculture production, which is partly

offset by lower timber production and lower output from

plantations).

Figure 23: Simulation results, Index (2000 = 1) for GDP of the Poor and per capita real disposable income: Scenario 4


6. Policy Implications based on Scenario Analysis

The most applicable and relevant assumptions were modeled in

the four scenarios above based on what the officials of Kalteng

and other stakeholders required in terms of policy analysis and

potential impacts. Based on the results, a reduction in plan-

tations in particular leads to the greatest benefits for natural

capital in Kalteng, while also projecting that in this scenario

agricultural production, GDP and food balance will at least be

equivalent to BAU.

Thus, economic expansion and development policies in Kalteng

need to take into account how a reduction in the growth of

plantations can be incorporated into policy agenda’s and how

revenue generation and GDP growth can still be met without

significant dependence on plantations (for the purpose of this

analysis and due to comparatively low numbers sustainable

plantations have not been included). Moreover, such a corre-

lation of reductions in plantations with better incomes of the

poor (with higher agriculture production values in place) justi-

fies such a policy action.

Economic, social and environmental implicationsIn addition, based on the three indicators it is clear for policy-

makers in Kalteng to see that from an economic perspective

green agriculture, forest, tourism and fisheries are important

sectors to invest in to generate sustainable revenue for the prov-

ince. Overlooking the significance of these sectors would lead

to a need for local governments in Kalteng to meet the fiscal

growth gaps by bringing in industries that could bring finances

only in the short-term because they would not be based on the

actual natural assets that Kalteng has.

Socially, the ability of communities i.e. households that are de-

pendent on forests and rivers in Kalteng need the value of these

natural resources to be recognized and managed more sustain-

ably. Letting these resources degrade or exploiting them by

increasing mining licenses or other types of extraction based

activities would significantly affect the livelihoods of rural pop-

ulations in Kalteng. This would have further negative impacts

by adversely affecting their ability to withstand unpredictabil-

ity in climatic events, afford education and healthcare for their

families and get sufficient nutrition on a daily basis. Attempting

to substitute for these natural benefits would mean added costs

for Kalteng’s government.

In terms of the environment, KT-GEM clearly shows that un-

der a business as usual scenario the natural capital of Kalteng

is not only getting degraded and showing as GDP growth, but

that there is sufficient potential in various sectors, which are

also sectors that will lead towards a green economy, to create

economic growth and development for Kalteng. These sectors

directly already support employment, health, climate resilience

of communities and poverty alleviation, which can be better

managed.


7. Value Addition for BAPPEDAKT-GEM provides Kalteng officials with a “toolkit” that defines

the steps to implement a green economy and specifically sup-

ports the following assessments.

Officials can ensure increased revenue generation and growth

due to the fact that KT-GEM captures the value that natural

capital is providing to existing economic sectors of Kalteng,

thereby, helping to identify where incentives need to be provid-

ed to improve flows from ecosystem services. This can benefit

industries that are dependent on natural capital as their asset

base and in return bring prosperity to the region if sustainable

management of environmental resources is mainstreamed into

corporate practices. In addition, officials have the ability to

generate sector specific scenarios in KT-GEM, which can en-

able them to make targeted and detailed projections about the

impacts of certain planned interventions in a holistic fashion.

Relevant policy actions can be implemented with better im-

pacts at the household level, as GDP of the Poor reveals nature

based components of incomes. This can provide policymakers

seeking to reduce poverty levels in Kalteng with options that

strengthen market connections established by rural house-

holds. Focusing on such prevailing linkages that support liveli-

hoods is likely to generate better poverty alleviation, as it does

not include first destroying the resources of the poor and then

creating new opportunities for them in industries, factories,

contract labour, etc, that can be more expensive for the govern-

ment to undertake. Such integration of GDP of the Poor into

Kalteng’s policies would also mean that it would be the first

province to actively put into practice indicators for the achieve-

ment of the proposed Sustainable Development Goals (post-

2015).

At an administrative level, the indicators calculation process

of KT-GEM creates opportunities for capacity building and

knowledge generation amongst officials that become respon-

sible for reviewing the condition of natural resources and in-

comes of the poor in their jurisdictions and then establish da-

tabases of information. The latter ensures that Kalteng has a

robust center of statistics as gaps will be determined through

the KT-GEM utilisation process. Overall, KT-GEM can out-

line green economy trajectories that are suited to the context

of Kalteng and which can be utlised to replicate similar model

development in other provinces of Indonesia and present solu-

tions that are realistic, competitive and sustainable in nature.


Mining: The production of coal, gas and minerals should be dis-

aggregated, but data are lacking. As a result, more data

should be collected on (1) resources, (2) reserves, (3) pro-

duction, (4) value added, (5) land use, (6) toxic waste (or

environmental impacts) and (7) employment creation.

Further, there is interest in exploring land use and extrac-

tion in more detail, by geographical area (within Kalteng).

The value added per ton produced can be changed in the

model and user interface, meaningthat the economic value

of production per ton may be higher or lower going for-

ward. This may reflect a different mix of production, and/

or a change in the global value of mining production (e.g.

for exports).

Toxic waste is important, but data are lacking. While toxic

waste is currently included in the model, results are not

presented. This could be a priority for further development

of the model.

Land use: There is interest in further disaggregating land use, possi-

bly linking KT-GEM to a GIS map (at least by district) for

better exploring land use and mining activities.

GDP of the Poor:GDP of the poor represents household income, per house-

hold and per month (it is not value added, nor disposable

income at the household level). GDP of the poor is esti-

mated using results of a survey carried out in Kalteng, and

it is projected assuming that a change in natural capital will

impact (proportionally) the whole population (and hence

the poor) in Kalteng. Further, the GDP of the Poor cal-

culation currently does not impact other variable sin the

model, and can be considered an output indicator.

For this reason, the results are presented as relative to the

baseline, to assess changes driven by policies and assump-

tions, rather than in absolute numbers. This is also more

coherent because the total number of household by group

is not available.

It is important to note that the GDP of the Poor model is

a simplification of reality. In fact, the potential to move

from one occupation to the other (and the possibility to

switch from one natural resource use to the other to secure

more income) is not taken into account in the mode. As in

the case of land use, spatial disaggregation is not included

in the model, making so that all the projections refer to

average results (of province-wide trends), on one sample

household by type.

GDP of the Poor and Green GDP are consistent, and more

coherent than GDP and GDP of the Poor (as they both in-

clude the depletion of natural capital). As a consequence,

they can be directly compared.

Fisheries:Interventions of the fishery sector include the removal of

vessels, and the preservation of areas that support spawn-

ing. The former does not imply the direct replacement of

the vessels removed with new ones (it is, in fact a net re-

duction). The purchase of new vessels, with a higher tech-

nological level leading to higher productivity (more effec-

tive fish catch), can be introduced as a new intervention.

Caveats and recommendations for model use


Green Jobs:The estimation of the creation of green jobs is complex,

and requires making several considerations beyond the

analysis of the results projected by KT-GEM. Any model-

ing approach has limitations (especially in relation to the

soft factors affecting green jobs creation), and the extent to

which the model is useful can be measured by considering

whether it stimulates conversations and inform decision

making. In this regard, simplifications are made in KT-

GEM, to simulate “what if ” scenarios that can highlight

where the opportunities are, so that model users can de-

sign additional interventions to reach desired goals.

On the other hand, the limitations of the use of models

(with respect to how policy implementation would take

place in reality) have to be taken into account, and comple-

ment the considerations made when assessing model re-

sults. Specifically:

Core conventions on the right to organise: C87

Freedom of Association and Protection of the Right

to Organise Convention, 1948; C98 Right to Orga-

nise and Collective Bargaining Convention, 1949:

The realization of the right to organise and bar-

gain collectively – through unions, cooperatives,

employers orgs – is important as it would allow to

remove potential market distorsion (e.g. providing

access to markets).

Core conventions on forced labour: C105 Abolition

of Forced Labour Convention, 1957; C29 Forced

Labour Convention, 1930: the ILO reports that rep-

resentatives from the workers organizations have

raised issues in regard to forced labour on palm oil

plantations in Central Kalimantan. This is an exist-

ing distortion that needs to be resolved in order to

optimise green job creation.

Core conventions on discrimination: C100 Equal

Remuneration Convention, 1951; C111 Discrimi-

nation (Employment and Occupation) Convention,

1958: the gender pay gap and the need for equal

remuneration for equal work is not explicitly in-

cluded in KT-GEM simulations. Disability is also

an issue not explicitly taken into account

Core conventions on child labour: C138 Minimum

Age Convention, 1973; C182 Worst Forms of Child

Labour Convention, 1999: The LFS estimate does

not include child labour, as the labour force survey

only captures data of the working age population

(15 and above), and so does KT-GEM.

In summary, it is found that labour markets don’t adjust

quickly, or at least not as fast as other variables included in

the model (e.g. land use), particularly if decent work vari-

ables are taken into consideration. A process of improving

the capacity of labour market institutions is required – la-

bour inspection, vocational training, wage boards, BPJS II,

employment services, etc – in order to realise the change in

the labour market towards decent work. This process can-

not be triggered by sectoral policies alone (e.g. conversion

of conventional agriculture land to sustainable practices),

and requires dedicated policy design and action.


Model CustomizationAfter holding meetings and training sessions with officials

from Central Kalimantan the following modifications were

made to the model, further customizing it to the local con-

text of Kalteng.

Forestland: a new set of forest-related modules in-

cluding indicators of land use, timber production,

ecosystem goods and services were created with

the direct support of training participants. Original

equations were replaced to better represent the sta-

tistical classification used for land use (production

forest, comprising production, limited production

and converted production forest; conservation for-

est; and protected forest), and better represent the

key drivers of deforestation. As a result, deviating

from national statistics on land use we were able to

project land cover changes and show that defores-

tation could occur in Conservation and Protected

Forest as the community encroaches forest areas

due to their livelihood needs.

Population: the demographic module of KT-GEM

was improved to include more endogenous fac-

tors, specifically emphasizing migration. In fact, it

was observed that migration is primarily driven by

employment opportunities. As a result, a feedback

loop was added linking employment to population,

as well as to the economy (i.e. through labor pro-

ductivity). Employment is, therefore, now affected

by capital investment and influences population

and GDP as well. Population in turns affects the

demand of natural resources, which impacts both

GDP and Green GDP.

GDP: the formulation of GDP was modified to ex-

plicitly include feedback loops relating to health

and education expenditure as drivers of labor pro-

The Development of KT-GEM

KT-GEM Components

Text Box 1: KT-GEM model development summary


ductivity, but most importantly to more fully cap-

ture the several relations existing between natural

capital and the economy. Specifically, the produc-

tion of natural resources (e.g. fish and timber) di-

rectly affects sectoral GDP; the state of stocks in-

fluences productivity (e.g. in the case of ecological

scarcity, likelihood of extreme weather events); and

the economic valuation of natural resources stocks

and flows allows for the estimation of Green GDP.

Energy: despite being of regional nature (with

supply from hydropower plants reaching several

provinces), hydropower capacity, including both

micro hydro and larger projects, is very relevant for

Kalteng. For this reason detail has been added on

power supply, which now includes thermal capaci-

ty, hydropower and renewables. Further, an explicit

link was added between the energy bill (i.e. the cost

of energy consumption) and economic productiv-

ity.

The modifications mentioned above support the analysis of

several green economy interventions, as well as their cross-

sectoral outcomes. These includes policies on land use, af-

fecting forest cover and agriculture/timber production, as

well as the availability of ecosystem good and services, and

overall impacts on macro indicators such as emissions and

economic production (i.e. GDP and GDP of the Poor).

Type of Village Total average ecosystem based Non Cash Income(% of total income)

Total average ecosys-tem based Cash and Non Cash Income (% of total income)

1. Initiation meeting with Vice Governor of Kalimantan Tengah

1. Bappeda (Provincial Planning agency)2. Provincial Secretary3. Mining and Energy agency4. Environmental agency5. Forestry agency6. Public works agency7. Agriculture agency8. Plantation agency

12 September 2014

2. System Dynamics Modelling Training – Phase 1

1. Bappeda (Provincial Planning agency)2. Provincial Secretary, Bureau Administrative and Devel-opment3. Provincial Secretary, Bureau Economy4. Agriculture agency5. Forestry agency6. Plantation agency7. Environmental agency8. Agriculture agency9. Mining and Energy agency10. Provincial Capital and LicensingManagement agency11. University of Palangkaraya12. UNORCID

22-24 September 2014

Consultations and Meetings Conducted


Type of Village Total average ecosystem based Non Cash Income(% of total income)

Total average ecosys-tem based Cash and Non Cash Income (% of total income)

3. System Dynamics Modelling Training – Phase 2

1. Bappeda (Provincial Planning agency)2. Provincial Secretary, Bureau Administrative and Devel-opment3. Provincial Secretary, Bureau Economy4. Agriculture agency5. Forestry agency6. Plantation agency7. Environmental agency8. Agriculture agency9. Mining and Energy agency10. Provincial Capital and Licensing Management agency11. University of Palangkaraya12. UNORCID

Wednesday, 29/10/2014

4. Technical consulta-tion with Provincial Government of Kali-mantan Tengah

1. Bappeda (Provincial Planning agency)2. Forestry agency3. Mining and Energy agency4. Plantation agency5. Agriculture agency6. Provincial Secretary, Bureau Administrative and Devel-opment7. Public Works agency8. Environmental agency

3 – 4 November 2014

5. Technical consulta-tion with University of Palangkaraya, Kali-mantan Tengah

1. Dean of Faculty Agriculture2. Lecturers 7 – 8 November 2014


Sukhdev P., Bassi A., Varma K. and Mumbunan S., (2014), Indonesia Green Economy Model (I-GEM), Full Project report, Pre-

pared under Low Emission Capacity Building Programme (LECB), United Nations Development Programme (UNDP), Jakarta,

Indonesia.

United Nations Development Programme (UNDP), (2014) LECB Indonesia Policy Note, I-GEM: Measuring Indonesia’s Transi-

tion towards a Green Economy, Indonesia.

RAD GRK, Kalteng (2012), Indonesia. http://www.unorcid.org/upload/doc_lib/20130207132847_RAD%20GRK%20grey.pdf

References


Contact Person:

Verania Andria

Programme Manager

United Nations Development Programme (UNDP)-Indonesia

Menara Thamrin Building, 9th Floor

Kav.3 Jl. M.H. Thamrin, Jakarta 10250, Indonesia

[email protected]

Tel: +62 (0) 2129802300

Implemented by: Supported by:

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