Current World Environment

Introduction

The challenges of agricultural development today are not only to meet food needs, but also must be more environmentally friendly. Food demand is expected to continue to increase with an increasing population.^1,2 The increasing need for food both from food crops, horticulture, plantations, andanimal husbandry spurs productivity in the agricultural sector which triggers an increase in greenhouse gas (GHG) emissions in the agricultural sector.^3–5 In Indonesia, the agricultural sector's GHG emissions are dominated by the land-based agriculture subsector (food crops, horticulture, and plantations) compared to the livestock subsector. Data from Kementerian Lingkungan Hidup dan Kehutanan (The Ministry of Environment and Forestry)⁶ in 2017 emissions from the land-based agriculture sector reached 98,956.86 GgCO2eq, while emissions from the livestock sector only amounted to 21,070.52 GgCO2eq.

To reduce the rate of GHG emissions in 2009 the Indonesian government issued a policy called Rencana Aksi Pengurangan Emisi Gas Rumah Kaca (RAN–GRK) (Decree for GHG Emission Reduction) where Indonesia is committed to reducing GHG emissions by 26% with domestic effort and by 41% using international assistancebased on estimates from BAU (Business as Usual).^7,8 Agricultural sector emissions are targeted to decrease by 21 MtCO2eq/year until 2030.^9,10 At the regional (provincial) level, each Provincial Government is required to issued Rencana Aksi Daerah Penurunan Emisi Gas Rumah Kaca (RAD-GRK) (Regional Action Plans for Emissions Reduction) which allows regional governments to participate in national action plans for reducing GHG emissions.¹¹ Participation at the provincial-level is by calculating mitigation potential, developing emission reduction strategies, selecting local level mitigation actions, and identifying key stakeholders/institutions and financial resources¹². The agricultural GHG emission reduction policy in Indonesia is consistentwith the global GHG emission reduction scenario policy, ¹³ stated that although after the Paris Agreement in 2015 which globally had a target of reducing emissions by 1 GtCO2eq/year in 2030, the target must still ensure global food security. RAD-GRK policies at the regional level and RAN-GRK at the national level are part of Nationally Appropriate Mitigation Actions (NAMAs) that are continuously reported to the UNFCC.^8,14,15

With the population reached 267.3 millions in 2018 and its growth about 1.2%/year in the past 5 year,Indonesia is one of the countries with the largest population in the world. The increased human activity due to population growth is one of the causes of greenhouse gas (GHG) emissions.¹⁶ In the agricultural sector, GHG emissions will increase along with economic growth, an increase in agricultural production and increase in population.¹⁷ The agricultural sector which is conducted in densely populated areas will produce agricultural cultivation that is not environmentally friendly. This is due to the use of chemical fertilizers which are increasingly inefficient so that they produce more GHG emissions.^18–20 In fact, the combination of income growth and population pressure on agricultural land is the main driver for the use of chemical fertilizers.²¹ In Indonesia, the development of an environmentally friendly agricultural sector, especially food crops, is constrained by the inefficiently of chemical fertilizers, especially in rice cultivation which has traditionally been the main emitter.¹²

Some scientists^22,23 explicitly refer to GHG emissions as an externality resulting from economic activities. One hypothesis about the relationship between environmental damage and economic growth is the Environmental Kuznets Curve (EKC). The basis of the EKC hypothesis is that economic growth or per capita income will increase environmental damage and then decrease in an inverted U curve pattern ²⁴. In the early stages of economic growth, pollution abatement was almost non-existent because people did not yet have a preference for environmental quality ²⁵. The relationship between economic growth in the agricultural sector and GHG emissions is varied. The study from²⁶shows that Agriculture Value Added has a positive relationship with emissions in high- and upper-middle-income countries, but in low-middle-income countries an increase in Agriculture Value Added will reduce CO2 emissions. While the studyby Balsalobre-lorente et al ²⁷shows that the GDP of the agricultural sector is influential in increasing emissions in BRICS countries.

EKC theory is an adaptation of the research conducted by³³initially to find out the relationship between economic growth and income inequality. Kuznets curve research has continued to develop since the 90s through various studies by^34–36 to seek the relationship between environmental damage and economic growth. The various studies have become the initial milestone in the development of the EKC hypothesis.

With the increasing anxiety about climate change, environmental degradation variables are then measured using GHG emission variables. Initial EKC research using GHG emissions as a variable for environmental damage was carried out, among others by^36–38 by using panel data. EKC research using panel data is growing, recent studies using panel data include;^39,40 confirmed EKC hypothesis in the countries of Europe;⁴¹ confirmed the EKC hypothesis in 14 African countries⁴²; searched in MIKTA countries (Mexico, Indonesia, South Korea, Turkey, and Australia) the results where the EKC hypothesis was rejected in this study;^43,44; conducted an EKC hypothesis study in ASEAN countries, the results of in the research from⁴³ EKC hypothesis was accepted and research from⁴⁴ the EKC hypothesis was rejected. For EKC research with panel data in one country, among others, conducted by⁴⁵; examined the EKC hypothesis in China where the EKC was confirmed in the eastern region and the central region and rejected in the western region^46,47; confirm the occurrence of EKC in China in all regions; while⁴⁸ confirm the EKC hypothesis in theUnited States.

Increasing global awareness on the issue of climate change and global warming has led many countries to tighten environmental regulations. The study of policies or regulations in relation to the EKC hypothesis is conducted among others⁴⁹; in European countries shows that with strict legal rules the number of turning points obtained will be lower so that the goal of environmental preservation will be more easily achieved;study from⁵⁰ concluded that energy regulation policies can reduce GHG emissions⁵¹; in hisstudies in the European Union, Middle East, and Africa countries, concluded that the more stable political conditions, the quality of regulations and the more effective governance will be able to reduce GHG emissions⁵²; testing the policy in Taipei in the form of regulations fee for disposal of plastic waste has proven to be able to preserve the environment; while studies from^47,53 in China shows that strict environmental regulations can reduce GHG emissions. A study from⁵⁴ in 8 OPEC member countries concluded that good governance policies would reduce GHG emissions. Conversely, bad governance due to a high corruption index, it will affect horribly the quality of the environment.^55–57

Generally, countries with high populations will produce large emissions. Population growth increases the number of consumers and the level of consumption thereby driving growth in GHG emissions.⁵⁸ In the EKC hypothesis population variables are used in various forms including; the population density variable used by^59,60 who concluded that the population density variable increases GHG emissions; urbanization variable used by^61–64 whoconcluded that urbanization increases emissions except in research by⁶⁴ who concluded that urbanization will actually reduce emissions; the total population variable is used by⁶⁵ where this variable increases emissions in the long run, but has no effect in the short run; population growth variable used by^31,66 whoconcluded that the variable population growth increases CO2 emissions.

This study aims to determine the impact of regional and population policies in reducing GHG emissions in the EKC hypothesis of the land-based agriculture sector (food crops, horticulture , and plantations) in Indonesia. The EKC hypothesis research in the land-based agriculture sector in Indonesia also uses regional economic growth variables as endogenous variables. This research provides a novelty in the form of a special EKC hypothesis in the land-based agriculture sector (food crops, horticulture , and plantations). The use of population growth variables, comparison of emissions for the island of Java as the main producer of rice in Indonesia, to regional policies on reducing GHG emissions in the agricultural sector are a novelty addition in this study. Various studies on GHG emissions using the EKC hypothesis in Indonesia have been conducted before,²⁸ used coal consumption as the dependent variable while the urbanization and trade openness variableswere used as exogenous variables²⁹; searched the effect of renewable energy on EKC;³⁰ examined the effects of energy consumption, financial development and international trade on EKC,³¹ examined the EKC hypothesis with energy consumption and population growth regressor variables. Various results of these studies concluded thatthe EKC hypothesis occurred in Indonesia. While research from³² concluded that there is no evidence ofthe EKC hypothesis in Indonesia.

Data and Methodology

Data

Data obtained by various sources, data on CO2 emissions from the land-based agriculture sector were obtained from the Ministry of Environment and Forestry,⁶ labor data were obtained from the Ministry of Agriculture,⁶⁷ data on economic growth in the agricultural sector using the value of Gross Regional Domestic Products(GRDP)and population growth data obtained from the Central Statistics Agency ( BPS)⁶⁸. Provincial government policy data on reducing greenhouse gas emissions were obtained from the Badan Perencanaan Pembangunan Nasional (National Development Planning Agency) (Bappenas).⁶⁹ Following the availability and reliability of the data, only 31 Provinces of 34 Provinces in Indonesia were used from 2009 - 2017.

Methodology

The econometrics model in this study uses the approach taken by Dinda.⁷⁰ The focus of this research is to get the effect of population growth and provincial-level GHG emission reduction policies based on the EKC hypothesis. The equation used in this study is:

Model 1: 

To find out the effect of GHG emission reduction policies, equation (2) is made, which is :

Model 2 :

Equations (1) and (2) are converted into equations (3) and (4) based on the Generalized Method of Moments (GMM) two-step system equation developed by ⁷¹to check the EKC hypothesis as previously used by^53,72 :

Model 1 :

Model 2 :

where i is the province (i = 1,2, ........, 31) and t is the time period (t = 2009 - 2017), v is the effect of the panel level and É› is the term for random error. CO2 is the carbon emission of the land-based agriculture sector (food crops, horticulture, and plantations) of Indonesia per capita labor (kgCO2eq/capita), with explanatory variables consisting of;GRDP and GRDP2 is the value GRDP of the land-based agriculture/laborcapita(Rp/capita(base year 2010)). PG is Population Growth (%), this variable in previous studies affected on emissions ^31,66. DJawa is a dummy for the island of Java (1 for the province in Java Island and 0 for outside Java), Java was chosen because it has the largest number of agricultural land-basedworkers. DP is the Dummy Policy for the year in which the Regional Action Plan for Reducing Greenhouse Gas Emissions Reduction (RAD GRK) was established (0 before the regulation was passed and 1 after the regulation was passed). The statistical description of the variables used in this study can be seen in table 1.

Table 1. Descriptive statistic of variable

In general, based on research by Dinda ⁷⁰the estimation model examinesthe significance of the βi coefficient. Possible hypotheses are :

1. If β₁= β ₂ = 0 then there is no relationship between x and y
2. If β₁> 0 and β₂= 0, a linear and increasing relationship exists between x and y
3. If β₁< 0 and β₂ = 0, a linear and descending relationship exists between x and y
4. If β₁> 0, β₂< 0, there is an inverse U relationship between x and y, so EKC occurs
5. If β₁< 0, β₂> 0, U-shaped curve occurs.

where the turning point of gross regional domestic product per capita is expected to be discovered through

The panel data used in this study has a large cross-section type, but with little time span. This causes stochastic disorders related to exogenous explanatory variables and endogenous explanatory variables ⁷³. So GMM is used to control the potential for the endogeneity of variables. To test the model according to criteria from ⁷⁴then the AR (1) and AR (2) tests, to examine the hypothesis of no serial correlation, are also presented. Also, the Sargan test was also carried out to test the validity of the instrument.

Result and Discussion

Before conducting panel data regression using the system GMM estimator, a stationarity testmust be performed on each variables^75–77use the unit root test both the common unit root test LLC (Levin, Lin, Chu) by ⁷⁸and individual IPS root unit tests by ⁷⁹. The stationary test is carried out on all variables except the dummy variable, the results of the stationarity test in table 2 show that all variables have been stationary at the level or at the first difference.

Table 2: Test the root unit for stationarity

Source: Authors compilation

***,**,* : significant at 1%, 5% and 10% respectively

After obtaining that the variable used passed the stationarity test then the equation 1 model and model 2 were then tested according to the GMM System method. GMM test results in table 2 show that model 1 and model 2 can be seen from the autocorrelation test (AR (1) and AR (2)) and the Sargan test. In the first difference residuals test where the value of AR (1) is significant so H0 is rejected and the value of AR (2) is not significant so that H0 is accepted so that model 1 and model 2 do not experience autocorrelation problems. To test the validity of the models, the Sargan test is used where H0 states that the variables used that have over-identifying restrictions are rejected so that the validity of model 1 and 2 are accepted.

Table 3: Result of System GMM estimation

Notes : *,**,***10, 5 and 1 percent level of significance

System GMM test results in both model 1 and model 2 show that the previous year's emissions also contributed to the current year's emissions. These results are consistent with similar studies using the GMM model in which the previous year's emissions had a positive and significant effect.^53,80. In model 1, an increase of 1 kgCO2eq/capita in the previous period will increase current year emissions by 0.57 kgCO2eq/capita. Whereas in model 2 an increase of 1 kgCO2eq/capita in the previous period will increase current year emissions by 0.60 kgCO2eq/capita.

The GMM model also shows that economic growth represented by the GRDP/capita has a very significant effect on GHG emissions in the agricultural sector. Economic growth through GRDP of Rp. 1,000/capita will increase agricultural sector GHG emissions by 0.1052 kgCO2eq/capita in model 1 and 0.1027 kgCO2eq/capita in model 2. Although not in the agricultural sector, the effect of economic growth on GHG emissions in Indonesia is according to previous research.^{28–30,32,81} As for research on the agricultural sector, economic growth also affects agricultural sector emissions in France, Spain, and Portugal,⁸² Bulgaria and the Czech Republic,⁸³ Iran⁸⁴ and China.⁸⁵ The GRDP2 results are negative and significant at model 1 and model 2 according to research from⁷⁰ the EKC hypothesis was confirmed in the land-based agriculture sector in Indonesia. Confirmation of the EKC hypothesis of the agricultural sector in Indonesia is consistent with previous EKC hypothesis research in Indonesia, although it is not specific about the EKC hypothesis of the agricultural sector.^28–30,86

The dummy policy variable in model 2 shows that this variable has a negative and significant effect on GHG emissions in the agricultural sector, this conclusion is consistent with previous research namely.^47,49–53 This shows that the regulation on GHG emission reduction at the provincial-level (RAD GRK) can reduce GHG emissions in the agricultural sector. In the Decree for GHG Emission Reduction (RAN GRK) document, the Indonesian government is targeted to reduce GHG emissions in the agricultural sector by 21MtCO2eq/year by 2030.⁹ Data on agricultural sector emissions (including livestock and forestry) from 2009-2016 show that Indonesia has succeeded in reducing GHG emissions by an average of 6.33 MtonCO2eq compared to Business as Usual projections.⁸⁷

Population growth in Indonesia has a negative and significant coefficient. In model 1 each increase in population growth by 1 % will reduce agricultural sector GHG emissions by 13.86 kgCO2eq/capita, whereas in model 2 each increase in population growth of 1 % will reduce agricultural sector GHG emissions by 17.77 kgCO2eq/capita. This result is different from the research by Alam et al and Begu et al^31,66 for the general sector where increased population growth will increase GHG emissions. For the Agriculture sector^17,88 also gave different results with this study. The difference in the results of this study with previous research is possible because the focus of this study is only on the land-based agriculture sector. Besidethe land-based agriculture sector in Indonesia, the increase in population and economic growth in Indonesia triggers the flow of agricultural land conversion. Many agricultural lands have turned into residential areas, industrial areas, and others. From 2000 - 2015 from 9 provinces with the largest paddy fields in Indonesia, the average land conversion was 96,512 hectares annually.^89–91 The flow of land conversion caused by population growth has caused a decline in agricultural land in Indonesia.

The dummy variable for Java in both model 1 and model 2 shows that this variable influences emissions. This shows that there are real differences between the agricultural sector's GHG emissions produced in Java and outside Java. According to data from Ministry of Environment and Forestry,⁶ emissions from rice cultivation and land management dominate GHG emissions from the land-based agriculture sector in Indonesia. Java Island has the most extensive rice fields compared to other islands in Indonesia. The area of paddy fields on the island of Java reaches 3,223,812 ha, equivalent to almost 40 % of paddy fields in Indonesia.⁹² This can be an explanation for the significance of the Java dummy variable.

One of the main issues of research on the EKC hypothesis is the turning point. Turning points in this study will occur when the GRDP/capita at Rp. 44,201,608.67/capita in model 1 and Rp. 43,888,888.88/capita in model 2. Lower turning points in model 2 indicate that government policy or regulation variables can bend the EKC curve so that turning points are faster to obtain. By calculating GRDP using the base year 2010, the turning point value in model 1 is equivalent to 4,916.21 USD/capita and 4,881.42 USD/capita in model 2. The RAD GRK policy is also able to accelerate the achievement of turning points so that environmental aspects of sustainability are more quickly obtained. The turning points generated in this study are consistent with various previous studies. Whereas turning points in the EKC study in the agricultural sector have not been done much, but the results of this study are not much different from the study⁸⁴ obtained by the agricultural sector's turning point at4,711 USD, 5,424 USD and 4,920 USD. In comparison, the turning point on the general sector EKC hypothesis in Indonesia is equal to 7,729 USD/capita²⁹. In the ASEAN region a turning point is obtained at4,685 USD/capita⁴³; Asian region obtained at 8,600 – 11,600 USD/capita⁹³; as well as developing countries obtained between 928.88 USD/capita – 8,910 USD/capita.^94–96 The GRDP of the land-basedagricultural sector in 2017 in Indonesia averaged Rp. 28,782,788.73/capita or equivalent 3,201.28 USD/capita, so the turning point in Indonesia has not been reached. . The EKC hypothesis confirmed in this study shows that economic growth will increase agricultural sector GHG emissions in Indonesia. However, after turning points are exceeded, GHG emissions from the agricultural sector will decline.Therefore, the results of this study can provide an overview for policymakers to encourage economic growth in the agricultural sector in Indonesia. In addition to encouraging the welfare of farmer’s economic growth, also in the long run after achieving turning points emissions will decrease.

Conclusion and Policy Implication

The results of this study indicate that economic growth affects the increase in GHG emissions from the land-based agriculture sector. Whereas population growth causes reduced GHG emissions from the land-based agriculture sector due to this variable causing the conversion of agricultural land. The land-based agriculture sector in Java is proven to produce greater GHG emissions than islands outside Java.

The government policy variables in the form of RAD-GRK at the provincial-level in this study proved to be able to reduce GHG emissions in the agricultural sector. In implementing RAD-GRK policies, it is evident that the agricultural sector is sufficiently prepared to implement various mitigation policies ⁹⁷. A study from⁹⁸shows that in 2030 Indonesia is estimated to be able to reduce emissions in the agricultural sector by 47 MtCO2eq/year far greater than the target of 21MtCO2eq/year. Further study of the influence of RAD-GRK policy factors in each province will greatly help to provide a better figure.

The study also ensured that GHG emissions from the land-based agriculture sector produced by farmers in Java were greater than farmers outside Java. The large size of paddy fields in Java is the cause of the greater GHG emissions compared to other islands in Indonesia. The EKC hypothesis confirmed in this study shows that in economic growth will increase agricultural sector GHG emissions in Indonesia. However, after turning points are exceeded, GHG emissions in the agricultural sector will decline. Based on the results of this study, the Indonesian government needs to implement an abatement policy, especially for agriculture in Java. The results of this study indicate that the agricultural sector in Java produces more per capita emissions compared to outside Java.

Acknowledgements

We would like to thank to the Department of Agricultural Socioeconomics, Faculty of Agriculture, and Universitas Gadjah Mada for their assistance in conducting this research. The authors also express sincere thanks and gratitude for the support for the research from Central Java Provincial Government and  Research Directorate, Universitas Gadjah Mada especially for the publication of this article.

Funding Source

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The authors do not have any conflict of interest.

Refferences

1. Valin H, Sands RD, van der Mensbrugghe D, et al. The future of food demand: understanding differences in global economic models. Agric Econ. 2013;45:51-67. doi:10.1111/agec.12089
2. Schneider UA, Havlík P, Schmid E, et al. Impacts of population growth, economic development, and technical change on global food production and consumption. Agric Syst. 2011;104(2):204-215. doi:10.1016/j.agsy.2010.11.003
3. Valin H, Havlík P, Mosnier A, Herrero M, Schmid E, Obersteiner M. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ Res Lett. 2013;8(3):1-9. doi:10.1088/1748-9326/8/3/035019
4. Reay DS, Davidson EA, Smith KA, et al. Global agriculture and nitrous oxide emissions. Nat Clim Chang. 2012;2(6):410-416. doi:10.1038/nclimate1458
5. Bennetzen EH, Smith P, Porter JR. Agricultural production and greenhouse gas emissions from world regions — The major trends over 40 years. Glob Environ Chang. 2016;37:43-55. doi:10.1016/j.gloenvcha.2015.12.004
6. Kementerian Lingkungan Hidup dan Kehutanan (Ministry of Environment and Forestry). Emisi dari Sektor Pertanian (Emission from Agriculture). Signsmart. http://signsmart.menlhk.go.id/v2.1/menu-emisi/. Published 2019. Accessed June 20, 2019.
7. Morizane J, Enoki T, Hase N, Setiawan B. Government Policies and Institutions for Climate Change Mitigation and Its Monitoring , Evaluation , and Reporting. In: Kaneko S, Kawanishi M, eds. Climate Change Policies and Challenges in Indonesia. Tokyo: Springer Japan; 2016:27-64. doi:10.1007/978-4-431-55994-8
8. Brown J, Peskett L. Climate Finance in Indonesia : Lessons for the Future of Public Finance for Climate Change Mitigation. Bonn, Germany; 2011.
9. Hasegawa T, Matsuoka Y. Climate change mitigation strategies in agriculture and land use in Indonesia. Mitig Adapt Strateg Glob Chang. 2015;20(3):409–424. doi:10.1007/s11027-013-9498-3
10. Bappenas. Developing Indonesian Climate Mitigation Policy 2020 - 2030. (Tilburg X van, Rawlins J, eds.). Jakarta: Bappenas; 2015.
11. Rahayu R. Policy Development for Effective Transitions to Climate Change: Adaptation at the Indonesian Local Government Level. 2012.
12. Leimona B, Amaruzaman S, Arifin B, et al. Indonesia’s ‘Green Agriculture’ Strategies and Policies: Closing the Gap between Aspirations and Application. Nairobi; 2015.
13. Wollenberg E, Richards M, Smith P, et al. Reducing emissions from agriculture to meet the 2 ° C target. Glob Chang Biol. 2016;22:3859-3864. doi:10.1111/gcb.13340
14. Bappenas. Kerangka Kerja Indonesia Untuk Nationally Appropriate Mitigation Actions (NAMAs). Jakarta: Bapenas; 2013.
15. Butt S, Lyster R, Stephens T. Climate Change and Forest Governance. Lessons from Indonesia. Oxon: Routledge Taylor and Francis Group; 2015. doi:10.1641/0006-3568(2001)051
16. Chandrappa R, Gupta S, Kulshrestha UC. Coping with Climate Change Principles and Asian Context. Heidelberg: Springer; 2011.
17. Van Beek CL, Meerburg BG, Schils RLM, Verhagen J, Kuikman PJ. Feeding the world’s increasing population while limiting climate change impacts: linking N2O and CH4 emissions from agriculture to population growth. Environ Sci Policy. 2010;13(2):89-96. doi:10.1016/j.envsci.2009.11.001
18. Josephson AL, Ricker-Gilbert J, Florax RJGM. How does population density influence agricultural intensification and productivity? Evidence from Ethiopia. Food Policy. 2014;48:142-152. doi:10.1016/j.foodpol.2014.03.004
19. Willy DK, Muyanga M, Jayne T. Can economic and environmental benefits associated with agricultural intensification be sustained at high population densities? A farm level empirical analysis. Land use policy. 2019;81(May 2018):100-110. doi:10.1016/j.landusepol.2018.10.046
20. Ricker-Gilbert J, Jumbe C, Chamberlin J. How does population density influence agricultural intensification and productivity? Evidence from Malawi. Food Policy. 2014;48:114-128. doi:10.1016/j.foodpol.2014.02.006
21. Singh AP, Narayanan K. Impact of economic growth and population on agrochemical use: evidence from post-liberalization India. Environ Dev Sustain. 2015;17(6):1509-1525. doi:10.1007/s10668-015-9618-1
22. Chen J, Huang P, Mccarl BA, Shiva L, Texas A, Station C. Climate Change , Society , and Agriculture : An Economic and Policy Perspective. Encycl Agric Food Syst. 2014;2:294-306. doi:10.1016/B978-0-444-52512-3.00001-2
23. Stern N. Stern Review : The Economics of Climate Change. Cambridge: Cambridge University Press; 2006. doi:10.1257/aer.98.2.1
24. Todaro MP, Smith SC. Economic Development. Twelfth Ed. Boston: Pearson; 2015.
25. Bartz S, Kelly DL. Economic growth and the environment : Theory and facts. Resour Energy Econ. 2008;30:115-149. doi:10.1016/j.reseneeco.2007.06.001
26. Anwar A, Sarwar S, Amin W, Arshed N. Agricultural practices and quality of environment : evidence for global perspective. Environ Sci Pollut Res. 2019;26(15):15617–15630. doi:doi.org/10.1007/s11356-019-04957-x
27. Balsalobre-lorente D, Driha OM, Bekun FV, Osundina OA. Do agricultural activities induce carbon emissions ? The BRICS experience. Environ Sci Pollut Res. 2019;26:25218-25234.
28. Kurniawan R, Managi S. Coal consumption, urbanization, and trade openness linkage in Indonesia. Energy Policy. 2018;121:576-583. doi:10.1016/j.enpol.2018.07.023
29. Sugiawan Y, Managi S. The environmental Kuznets curve in Indonesia : Exploring the potential of renewable energy. Energy Policy. 2016;98:187-198. doi:10.1016/j.enpol.2016.08.029
30. Shahbaz M, Hye QMA, Tiwari AK, Leitão NC. Economic growth , energy consumption , financial development , international trade and CO2 emissions in Indonesia. Renew Sustain Energy Rev. 2013;25:109-121. doi:10.1016/j.rser.2013.04.009
31. Alam MM, Murad MW, Noman AHM, Ozturk I. Relationships among carbon emissions, economic growth, energy consumption and population growth : Testing Environmental Kuznets Curve hypothesis for Brazil, China, India and Indonesia. Ecol Indic. 2016;70:466-479.
32. Saboori B, Sulaiman J Bin, Mohd S. An Empirical Analysis of the Environmental Kuznets Curve For CO 2 Emissions in Indonesia : The Role of Energy Consumption and Foreign Trade. Int J Econ Financ. 2012;4(2):243-251. doi:10.5539/ijef.v4n2p243
33. Kuznets S. Economic Growth and Income Inequality. Am Econ Rev. 1955;45(1):1-28.
34. Grossman GM, Krueger AB. Economic Growth and The Environtment. Q J Econ. 1995;110(2):353-377.
35. Grossman GM, Krueger AB. Environmental Impacts of a North American Free Trade Agreement. National Bureau of Economic Cambridge; 1991.
36. Shafik N, Bandyopadhyay S. Economic Growth and Environmental Quality. Time-Series and Cross-Country Evidenc. Washington DC; 1992. http://documents.worldbank.org/curated/en/833431468739515725/Economic-growth-and-environmental-quality-time-series-and-cross-country-evidence.
37. Carson RT, Jeon Y, Donald R. The relationship between air pollution emissions and income : US Data. Environ Dev Econ. 1997;2:433-450.
38. Holtz-Eakin D, Selden TM. CO 2 emissions and economic growth. J Public Econ. 1995;57:85-101.
39. Kasman A, Duman YS. CO2 Emissions , Eeconomic Growth , Energy consumption , Trade and Urbanization in new EU Member and Candidate Countries : A Panel Data Analysis. Econ Model. 2015;44:97-103. doi:10.1016/j.econmod.2014.10.022
40. Mert M, Bölük G, ÇaÄŸlar AE. Interrelationships among foreign direct investments, renewable energy, and CO2 emissions for different European country groups: a panel ARDL approach. Environ Sci Pollut Res. 2019;26(21):21495–21510. doi:10.1007/s11356-019-05415-4
41. Sarkodie SA. The invisible hand and EKC hypothesis: what are the drivers of environmental degradation and pollution in Africa? Environ Sci Pollut Res. 2018;25(22):21993-22022. doi:10.1007/s11356-018-2347-x
42. Bakirtas I, Cetin MA. Revisiting the environmental Kuznets curve and pollution haven hypotheses: MIKTA sample. Environ Sci Pollut Res. 2017;24(22):18273-18283. doi:10.1007/s11356-017-9462-y
43. Heidari H, Turan Katircioǧlu S, Saeidpour L. Economic growth, CO2 emissions, and energy consumption in the five ASEAN countries. Electr Power Energy Syst. 2015;64:785-791. doi:10.1016/j.ijepes.2014.07.081
44. Liu X, Zhang S, Bae J. The impact of renewable energy and agriculture on carbon dioxide emissions : Investigating the environmental Kuznets curve in four selected ASEAN countries. J Clean Prod. 2017;164:1239-1247. doi:10.1016/j.jclepro.2017.07.086
45. Guangyue X, Deyong S. An empirical study on the environmental kuznets curve for China’s carbon emissions: Based on provincial panel data. Chinese J Popul Resour Environ. 2011;9(3):66-76. doi:10.1080/10042857.2011.10685040
46. Kang Y, Zhao T, Yang Y. Environmental Kuznets curve for CO 2 emissions in China : A spatial panel data approach. Ecol Indic. 2016;63:231-239.
47. Yin J, Zheng M, Chen J. The effects of environmental regulation and technical progress on CO 2 Kuznets curve : An evidence from China. Energy Policy. 2015;77:97-108. doi:10.1016/j.enpol.2014.11.008
48. Aldy JE. An Environmental Kuznets Curve Analysis of U.S. State-Level Carbon Dioxide Emissions. J Environ Dev. 2005;14:48-72. doi:10.1177/1070496504273514
49. Castiglione C, Infante D, Smirnova J. Rule of law and the environmental Kuznets curve : evidence for carbon emissions. Int J Sustain Econ. 2012;4(3):254-269. doi:10.1504/IJSE.2012.047932
50. Lorente DB, Álvarez-Herranz A. Economic growth and energy regulation in the environmental Kuznets curve. Environ Sci Pollut Res. 2016;23(16):16478-16494. doi:10.1007/s11356-016-6773-3
51. Abid M. Does economic , financial and institutional developments matter for environmental quality ? A comparative analysis of EU and MEA countries. J Environ Manage. 2017;188(2):183-194. doi:10.1016/j.jenvman.2016.12.007
52. Chen C, Chen Y. Income effect or policy result : a test of the environmental Kuznets Curve. J Clean Prod. 2008;16:59-65. doi:10.1016/j.jclepro.2006.07.027
53. Chen H, Hao Y, Li J, Song X. The impact of environmental regulation, shadow economy, and corruption on environmental quality: Theory and empirical evidence from China. J Clean Prod. 2018;195:200-214. doi:10.1016/j.jclepro.2018.05.206
54. Ronaghi M, Reed M, Saghaian S. The impact of economic factors and governance on greenhouse gas emission. Environ Econ Policy Stud. 2019. doi:10.1007/s10018-019-00250-w
55. Leitão A. Corruption and the environmental Kuznets Curve : Empirical evidence for sulfur. Ecol Econ. 2010;69(11):2191-2201. doi:10.1016/j.ecolecon.2010.06.004
56. Zhang Y, Jin Y, Chevallier J, Shen B. The effect of corruption on carbon dioxide emissions in APEC countries : A panel quantile regression analysis. Technol Forecast Soc Chang. 2016;122:220-227. doi:10.1016/j.techfore.2016.05.027
57. Bimonte S, Stabile A. The Effect of Growth and Corruption on Soil Sealing in Italy: A Regional Environmental Kuznets Curve Analysis. Environ Resour Econ. 2019. doi:10.1007/s10640-019-00376-1
58. Satterthwaite D. The implications of population growth and urbanization for climate change. Environ Urban. 2009;21(2):545-567. doi:10.1177/0956247809344361
59. Abdouli M, Kamoun O, Hamdi B. The impact of economic growth, population density, and FDI inflows on CO 2 emissions in BRICTS countries: Does the Kuznets curve exist? Empir Econ. 2018;54(4):1717-1742. doi:10.1007/s00181-017-1263-0
60. Luo G, Weng JH, Zhang Q, Hao Y. A reexamination of the existence of environmental Kuznets curve for CO 2 emissions: evidence from G20 countries. Nat Hazards. 2017;85(2):1023-1042. doi:10.1007/s11069-016-2618-0
61. Dogan E, Turkekul B. CO2 Emissions , Real Output , Energy Consumption , Trade , Urbanization and Financial Development : Testing the EKC Hypothesis for the USA. Environ Sci Pollut Res. 2016;23:1203-1213. doi:10.1007/s11356-015-5323-8
62. Munir K, Ameer A. Effect of economic growth, trade openness, urbanization, and technology on environment of Asian emerging economies. Manag Environ Qual An Int J. 2018;29(6):1123-1134. doi:10.1108/MEQ-05-2018-0087
63. Demir C, Cergibozan R, Ari A. Environmental dimension of innovation : time series evidence from Turkey. Environ Dev Sustain. 2019. doi:10.1007/s10668-018-00305-0
64. Zhang S. Environmental Kuznets curve revisit in Central Asia : the roles of urbanization and renewable energy. Environ Sci Pollut Res. 2019;26:23386-23398. doi:doi.org/10.1007/s11356-019-05600-5
65. Mamun M Al, Sohag K, Mia MAH, Uddin GS, Ozturk I. Regional differences in the dynamic linkage between CO 2 emissions , sectoral output and economic growth. Renew Sustain Energy Rev. 2014;38:1-11. doi:10.1016/j.rser.2014.05.091
66. Begum RA, Sohag K, Abdullah SMS, Jaafar M. CO 2 emissions , energy consumption , economic and population growth in Malaysia. Renew Sustain Energy Rev. 2015;41:594-601. doi:10.1016/j.rser.2014.07.205
67. Kementerian Pertanian (Ministry of Agriculture). Statistik Pertanian (Agricultural Statistic). Jakarta: Kementerian Pertanian; 2018.
68. BPS (Badan Pusat Statistik). Statistik Indoesia2019. Jakarta: BPS; 2019.
69. Bappenas. Potret Rencana Aksi Daerah Penurunan Emisi Gas Rumah Kaca (RAD - GRK) (A Portrait of the Regional Action Plan for Reducing Greenhouse Gas Emissions (RAD - GRK)). Jakarta: Bappenas; 2014.
70. Dinda S. Environmental Kuznets Curve Hypothesis : A Survey. Ecol Econ. 2004;49:431-455. doi:10.1016/j.ecolecon.2004.02.011
71. Blundell R, Bond S. Initial conditions and moment restrictions in dynamic panel data models. J Econom. 1998;87:115-143.
72. Barra C, Zotti R. Investigating the non-linearity between national income and environmental pollution: international evidence of Kuznets curve. Environ Econ Policy Stud. 2018;20(1):179-210. doi:10.1007/s10018-017-0189-2
73. Ren S, Yuan B, Ma X, Chen X. International trade , FDI ( foreign direct investment ) and Embodied CO 2 Emissions : A Case Study of Chinas industrial sectors. China Econ Rev. 2014;28:123-134. doi:10.1016/j.chieco.2014.01.003
74. Arellano M, Bond S. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Rev Econ Stud. 1991;58(2):277-297.
75. Ito K. CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries. Int Econ. 2017;151:1-6. doi:10.1016/j.inteco.2017.02.001
76. Al-mulali U, Weng-wai C, Sheau-ting L, Mohammed AH. Investigating the environmental Kuznets curve ( EKC ) hypothesis by utilizing the ecological footprint as an indicator of environmental degradation. Ecol Indic. 2014;48:315-323. doi:10.1016/j.ecolind.2014.08.029
77. Hassan SA, Nosheen M. Estimating the Railways Kuznets Curve for high income nations—A GMM approach for three pollution indicators. Energy Reports. 2019;5:170-186. doi:10.1016/j.egyr.2019.01.001
78. Levin A, Lin CF, Chu CSJ. Unit root tests in panel data: Asymptotic and finite-sample properties. J Econom. 2002;108(1):1-24. doi:10.1016/S0304-4076(01)00098-7
79. Im KS, Pesaran MH, Shin Y. Testing for unit roots in heterogeneous panels. J Econom. 2003;115(1):53-74. doi:10.1016/S0304-4076(03)00092-7
80. Saidi K, Mbarek M Ben. The impact of income, trade, urbanization, and financial development on CO2 emissions in 19 emerging economies. Environ Sci Pollut Res. 2017;24(14):12748-12757. doi:10.1007/s11356-016-6303-3
81. Jafari Y, Othman J, Hassan A, Mohd S. Energy consumption , economic growth and environmental pollutants in Indonesia. J Policy Model. 2012;34(6):879-889. doi:10.1016/j.jpolmod.2012.05.020
82. Zafeiriou E, Azam M. CO 2 emissions and economic performance in EU agriculture : Some evidence from Mediterranean countries. Ecol Indic. 2017;81:104-114. doi:10.1016/j.ecolind.2017.05.039
83. Zafeiriou E, Sofios S, Partalidou X. Environmental Kuznets curve for EU agriculture : empirical evidence from new entrant EU countries. Environ Sci Pollut Res. 2017;24(8):15510-15520. doi:10.1007/s11356-017-9090-6
84. Alamdarlo HN. Water consumption, agriculture value added and carbon dioxide emission in Iran, environmental Kuznets curve hypothesis. Int J Environ Sci Technol. 2016;13(8):2079-2090. doi:10.1007/s13762-016-1005-4
85. Zhangwei L, Xungang Z. Study on relationship between Sichuan agricultural carbon dioxide emissions and agricultural economic growth. Energy Procedia. 2011;5:1073-1077. doi:10.1016/j.egypro.2011.03.189
86. Waluyo EA, Terawaki T. Environmental Kuznets Curve for Deforestation in Indonesia: An ARDL Bounds Testing Approach. J Econ Coop Dev. 2016;3:87-108.
87. Kementerian Lingkungan Hidup dan Kehutanan (Ministry of Environment and Forestry). Business as Usual. http://signsmart.menlhk.go.id/v2.1/bau/. Published 2019. Accessed June 20, 2019.
88. Long X, Luo Y, Wu C, Zhang J. The influencing factors of CO 2 emission intensity of Chinese agriculture from 1997 to 2014. Environ Sci Pollut Res. 2018;25(13):13093–13101.
89. Nuryantono N, Tongato A, Yusdiyanto S, Pasaribu SH, Anggraenie T. Land conversion and economic development in Jawa Barat Province: Trade off or Synergy? IOP Conf Ser Earth Environ Sci. 2017;54:12-17. doi:10.1088/1742-6596/755/1/011001
90. Rondhi M, Pratiwi PA, Handini VT, Sunartomo AF, Budiman SA. Agricultural land conversion, land economic value, and sustainable agriculture: A case study in East Java, Indonesia. Land. 2018;7(4):1-19. doi:10.3390/land7040148
91. Mulyani A, Kuncoro D, Nursyamsi D, Agus F. Analisis Konversi Lahan Sawah: Penggunaan Data Spasial Resolusi Tinggi Memperlihatkan Laju Konversi yang Mengkhawatirkan (Analysis of Paddy Field Conversion: The Utilization of High Resolution Spatial Data Shows an Alarming Conversion Rate). J Tanah dan Iklim. 2016;40(2):121-133. doi:10.1093/nq/s4-II.40.329-b
92. Central Bureau of Statistics (BPS). Statistik Indoesia (Indonesian Statistic) 2018 (in Indonesian). Jakarta: BPS; 2018.
93. Apergis N, Ozturk I. Testing Environmental Kuznets Curve hypothesis in Asian countries. Ecol Indic. 2015;52:16-22. doi:10.1016/j.ecolind.2014.11.026
94. Sarkodie SA, Strezov V. A review on Environmental Kuznets Curve hypothesis using bibliometric and meta-analysis. Sci Total Environ. 2019;649:128-145. doi:10.1016/j.scitotenv.2018.08.276
95. Aye GC, Edoja PE. Effect of economic growth on CO2 emission in developing countries: Evidence from a dynamic panel threshold model. Cogent Econ Financ. 2017;5(1):1-22. doi:10.1080/23322039.2017.1379239
96. Shahbaz M, Nasreen S, Ahmed K, Hammoudeh S. Trade openness–carbon emissions nexus: The importance of turning points of trade openness for country panels. Energy Econ. 2017;61:221-232. doi:10.1016/j.eneco.2016.11.008
97. Sugiri A. Preparedness in implementing action plan for reducing GHGs: The case of central java. Am J Environ Sci. 2015;11(1):13-27. doi:10.3844/ajessp.2015.13.27
98. Fujimori S, Siagian UWR, Hasegawa T, et al. An Assessment of Indonesia’s Intended Nationally Determined Contributions. In: Fujimori S, Kainuma M, Masui T, eds. Post-2020 Climate Action. Singapore: Springer; 2017:v-vi. doi:10.1007/978-981-10-3869-3
99. Castiglione C, Infante D, Smirnova J. Environment and economic growth: is the rule of law the go-between? The case of high-income countries. Energy Sustain Soc. 2015;5(1):1-7. doi:10.1186/s13705-015-0054-8