3KTGEMfinalreport

Kalimantan Tengah
Green Economy Model
(KT-GEM)
Pavan Sukhdev, Kaavya Varma, Andrea Bassi and Sonny Mumbunan
LECB INDONESIA
FINAL 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 ii
Executive Summary 1
1. I-GEM and its applicability for Provinces 2
2. Green Economy Indicators for Kalteng 3
3. Kalteng Green Economy Model (KT-GEM) 4
4. Results
- Business as Usual 6
- Green Economy Scenarios 22
7. Policy Implications based on Scenario Analysis 35
8. Value Addition for BAPPEDA 36
References 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).

i
Kalteng 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 Abbreviations
I-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

1
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.

3
2. Green Economy Indicators for Kalteng
Shifting 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)”.
Decent
Green
Jobs
GDP of
the Poor
Green
GDP
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
Transmigration
Birth Control Policy
GDP
Maximum Life Expectancy
Tech Rate Of Change
Goverment
Domestic Revenue Aggregate Tax Rate Time Series
Non Tax Revenue Share Of GDP
Households
Investment Share
Land Use
Food Per Person
Target Year For Agriculture Conversion Stop
Maximum Forest Conversion
Avoided Deforestation Policy Switch
Agriculture
Conventional Food Crop To Plantation Reduction Due To
Income Expectations
Organic Agriculture Investment Per Ha
Degraded Forest Conversion To Plantation
Sustainable Agriculture Target Ha
Agriculture Target Year
Conventional Agriculture Productivity
Sustainable Agriculture Productivity
Shifting Food Crop Productivity Table
Fishery
Fish Conservation Policy
Fish Conservation Policy Switch
Vessel Removal Policy
Vessel Removal Policy Switch
Mining
Land Use Per Million Metric Ton Mining Resources Fraction Recoverable

5
Assumptions Policies
Energy
Effect Of Improved Electrification On Electricity Demand
Per Person
Electricity Shortage Switch
Energy Efficiency Policy Switch
Energy Efficiency
Hydro Energy Policy Switch
Hydro Construction Start Year
Desired Hydro Capacity
Solar Energy Policy Switch
Solar Construction Start Year
Desired Solar Capacity
Tourism
Desired Tourism Arrivals Growth Rate
GDP Effect On Tourism Arrivals
Roads
Elasticity Of Road Accessibility Target To Population
Density
Forest Cover
Conversion Primary To Plant Forest
Effect Primary Forest
Second Forest Conversion
Effect Second Forest
Primary Forest Logging
Logging Second Forest
Productivity Of Plant Forest
Conservation Forest
Conversion Of Conservation Forest
Percentage Of Other Utilization
Convertible Forest
Effect Conversion Forest
Timber Produced By Logging
Forestry Economics
CO2 Price Per Ton
Value Added Forestry Table

4. Business as Usual
Population 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

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

9
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)

11
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

13
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.

15
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

17
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 Village
Total 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 households
43.63 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

19
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.
t Forests
t Changes due to economic activities
t Timber, Fuelwood, Non-timber Forest Products
Carbon
t Soil Conservation, Water Augmentation Flood
Prevention
t Ecosystem and Species Diversity Values
t Bio-prospecting Values (if relevant)
t Existence Value of Biodiversity
t Agricultural Cropland Pasture Land
t Freshwater
t Subsoil assets
t 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

21
Figure 11: KT-GEM BAU versus GE simulation

5. Green Economy Scenarios
Several 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:
t (Scenario 1) an improvement of agriculture productiv-
ity (both on food crop shifting land and on conventional
land),
t (Scenario 2) the elimination of conversion from forest
to agriculture land,
t (Scenario 3) the reduction of deforestation (reaching
a maximum of 2.5 million ha cumulatively from 2012)
and
t (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:
t The promotion of sustainable agriculture,
t The reduction of vessels and boats as well as fish stock
management interventions to support maintenance and
replenishment of the stock,
t Reforestation (in the same amount as deforestation of
primary and secondary forest) to curb the reduction of
forestland, and
t 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-
ductivity
This 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.

23
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

25
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 settlements
The 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

27
Figure 16: Simulation results, productivity per hectare and
crops and livestock GDP: Scenario 3
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.

Figure 17: Simulation results, food balance and
agriculture land: Scenario 3

29

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.

31
Figure 20: Simulation results, productivity per hectare
and crops and livestock GDP: Scenario 4
Average Yield Per Ha
2
1.5
1
0.5
0
5 5 5 5 5 5 5 5
4 4 4 4 4 4
4
4
3 3 3 3 3 3
3
3
3
2 2 2 2 2 2
2
2
2
1 1 1 1 1 1 1
1
1
2000 2004 2008 2012 2016 2020 2024 2028
Time (Year)
Ton/Ha
Average Yield Per Ha : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1 1
Average Yield Per Ha : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2 2
Average Yield Per Ha : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3 3
Average Yield Per Ha : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4
Average Yield Per Ha : BAU 18 Dec LU1 BAU 5 5 5 5 5 5
crops and livestock gdp
8e+12
6e+12
4e+12
2e+12
0
6
6
6
5
5 5 5 5
5 5
5
4
4 4 4 4
4
4
4
3
3
3 3 3
3
3
3
2
2
2 2 2
2
2
2
1
1
1 1 1
1
1
1
1
2000 2004 2008 2012 2016 2020 2024 2028
Time (Year)
Rp2000/Year
crops and livestock gdp : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1
crops and livestock gdp : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2 2
crops and livestock gdp : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3
crops and livestock gdp : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4
crops and livestock gdp : BAU 18 Dec LU1 BAU 5 5 5 5 5 5
crops and livestock gdp : Kalteng data v2 6 6 6 6 6 6

Figure 21: Simulation results, food balance and agriculture
land: Scenario 4
Agriculture Land
6 M
4.5 M
3 M
1.5 M
0
6
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2 2
1 1
1
1
1
1 1 1 1
2000 2004 2008 2012 2016 2020 2024 2028
Time (Year)
Ha
Agriculture Land : BAU 18 Dec LU1 + Pr +Ag_Sl + Dp+ Pl 1 1 1 1 1
Agriculture Land : BAU 18 Dec LU1 + Pr +Ag_Sl + Dp 2 2 2 2 2
Agriculture Land : BAU 18 Dec LU1 + Pr +Ag_Sl 3 3 3 3 3 3
Agriculture Land : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4 4
Agriculture Land : BAU 18 Dec LU1 BAU 5 5 5 5 5 5 5
Agriculture Land : Kalteng data v2 6 6 6 6 6 6

33
Forest Area
10 M
8.5 M
7 M
5.5 M
4 M
6 6
6
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
2 2
2
2
2
2
2 2
1 1
1
1
1
1 1 1 1
2000 2004 2008 2012 2016 2020 2024 2028
Time (Year)
Ha
Forest Area : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1 1
Forest Area : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2 2 2
Forest Area : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3 3
Forest Area : BAU 18 Dec LU1 + Pr 4 4 4 4 4 4 4
Forest Area : BAU 18 Dec LU1 BAU 5 5 5 5 5 5 5
Forest Area : Kalteng data v2 6 6 6 6 6 6 6
annual co2 emissions from forest
40 M
20 M
0
-20 M
-40 M
6
6
5
5
5
5 5 5 5
5
4
4
4
4 4
4 4 4
3
3
3
3 3
3 3 3
2
2
2
2 2
2 2
2
1
1
1
1 1 1
1 1 1
2000 2004 2008 2012 2016 2020 2024 2028
Time (Year)
Tons/Year
annual co2 emissions from forest : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp+ Pl 1 1 1 1
annual co2 emissions from forest : BAU 18 Dec LU1 + Pr + Ag_Sl + Dp 2 2 2 2
annual co2 emissions from forest : BAU 18 Dec LU1 + Pr + Ag_Sl 3 3 3 3 3
annual co2 emissions from forest : BAU 18 Dec LU1 + Pr 4 4 4 4 4
annual co2 emissions from forest : BAU 18 Dec LU1 BAU 5 5 5 5 5 5
annual 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

35
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 implications
In 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 BAPPEDA
KT-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.

37
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:
t 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).
t 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.
t 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
t 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.

39
Model Customization
After 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.
t 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.
t 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.
t 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.
t 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 Secretary
3. Mining and Energy agency
4. Environmental agency
5. Forestry agency
6. Public works agency
7. Agriculture agency
8. Plantation agency
12 September 2014
2. System Dynamics
Modelling Training –
Phase 1
2. Provincial Secretary, Bureau Administrative and Devel-
opment
3. Provincial Secretary, Bureau Economy
5. Forestry agency
10. Provincial Capital and Licensing
Management agency
11. University of Palangkaraya
12. UNORCID
22-24 September 2014
Consultations and Meetings Conducted

41
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
opment
3. Provincial Secretary, Bureau Economy
5. Forestry agency
10. Provincial Capital and Licensing Management agency
11. University of Palangkaraya
12. UNORCID
Wednesday, 29/10/2014
4. Technical consulta-
tion with Provincial
Government of Kali-
mantan Tengah
2. Forestry agency
opment
7. Public Works agency
3 – 4 November 2014
5. Technical consulta-
tion with University of
Palangkaraya, Kali-
mantan Tengah
1. Dean of Faculty Agriculture
2. 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

43
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
verania.andria@undp.org
Tel: +62 (0) 2129802300

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