Scenario analysis of the Indonesia carbon tax impact on carbon emissions using system dynamics modeling and STIRPAT model

This study aims to develop a system dynamic(SD)forecasting model based on the STIRPAT model to forecast the effect of an IDR 30 per kg CO_(2)e carbon tax on carbon emissions,estimate future carbon emissions under ten scenarios,without and with the carbon tax,and estimate the environmental Kuznets curve(EKC)to predict Indonesia’s carbon emission peak.Carbon emission drivers in this study are decomposed into several factors,namely energy structure,energy intensity,industrial structure,GDP per capita,population,and fixed-asset investment.This study included nuclear power utilization starting in 2038.The research gaps addressed by this study compared to previous research are(1)use of the ex-ante approach,(2)inclusion of nuclear power plants,(3)testing the EKC hypothesis,and(4)contribution to government policy.The simulation results show that under the carbon tax,carbon emissions can be reduced by improving renewable energy structures,adjusting industrial structures to green businesses,and emphasizing fixed asset investment more environmentally friendly.Moreover,the result approved the EKC hypothesis.It shows an inverse U-shaped curve between GDP per capita and CO_(2)emissions in Indonesia.Indonesia’s fastest carbon emission peak is under scenario seven and is expected in 2040.Although an IDR 30 per kg CO_(2)e carbon tax and nuclear power will take decades to reduce carbon emissions,the carbon tax can still be a reference and has advantages to implement.This result can be a good beginning step for Indonesia,which has yet to gain experience with a carbon tax that can be implemented immediately and is helpful to decision-makers in putting into practice sensible measures to attain Indonesia’s carbon emission peaking.This research provides actionable insights internationally on carbon tax policies,nuclear energy adoption,EKC dynamics,global policy implications,and fostering international cooperation for carbon emission reductions.

The driving force of water resource stress change based on the STIRPAT model:take Zhangye City as a case study

A prominent contradiction between supply and demand of water resources has restricted local development in social and economic aspects of Zhangye City,located in a typical arid region of China.Our study quantified the Water Resource Stress Index(WRSI)from 2003 to 2017 and examined the factors of population,urbanization level,GDP per capita,Engel coefficient,and water consumption per unit of GDP by using the extended stochastic impact by regression on population,affluence and technology(STIRPAT)model to find the key factors that impact WRSI of Zhangye City to relieve the pressure on water resources.The ridge regression method is applied to improve this model to eliminate multicollinearity problems.The WRSI system was developed from the following three aspects:water resources utilization(WR),regional economic development water use(WU),and water environment stress(WE).Results show that the WRSI index has fallen from 0.81(2003)to 0.17(2017),with an average annual decreased rate of 9.8%.Moreover,the absolute values of normalized coefficients demonstrate that the Engel coefficient has the largest positive contribution to increase WRSI with an elastic coefficient of 0.2709,followed by water consumption per unit of GDP and population with elastic coefficients of 0.0971 and 0.0387,respectively.In contrast,the urbanization level and GDP per capita can decrease WRSI by−0.2449 and−0.089,respectively.The decline of WRSI was attributed to water-saving society construction which included the improvement of water saving technology and the adjustment of agricultural planting structures.Furthermore,this study demonstrated the feasibility of evaluating the driving forces affecting WRSI by using the STIRPAT model and ridge regression analysis.

Analysis of factors influencing carbon emissions in the Yangtze River Delta region and projections of carbon peak scenarios

Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.

Ecological Footprint and Major Driving Forces in West Jilin Province, Northeast China

The environmental impact caused by local people （ecological footprint of consumption, EFc） and the actual environmental impact that the ecosystem burdens （ecological footprint of production, EFp） in West Jilin Province, Northeast China from 1986 to 2006 were evaluated by using ecological footprint （EF） method. And the major driving forces of EFc and EFp were analyzed by STIRPAT model. Both EFc and EFp showed increasing trends in 1986-2006, accompanied by decreasing ecological deficits but expanding ecological overshoots. Population （P）, GDP per capita （A1）, quadratic term of GDP per capita （A2）, urbanization （Tα1）, and quadratic term of urbanization （Ta2） were important influencing factors of EFc, among which Tα2 and Tα1 were the most dominate driving forces of EFc. A1, A2 and Tα2 were important influencing factors of EFp, among which A2 and A1 were the most dominate driving forces of EFp. In 1986-2006, the classical Environmental Kuznets Curve hypothesis did not exist between A2 and EF （both EFc and EFp）, but did between Tα2 and EF. The results indicate that enhancing the urbanization process and diversifying economic sources is one of the most effective ways to reduce the environmental impact of West Jilin Province. Moreover, importance should be attached to improve the eco-efficiency of resource exploitation and consumption.

Human Driving Forces:Analysis of Rocky Desertification in Karst Region in Guanling County,Guizhou Province

Karst rocky desertification is one of the major ecological and environmental problems that threaten the sustainable development of southwestern China. It is caused by irrational and intensive land-use patterns in karst geo-ecological environment. Therefore, it is vital to identify how human forces work on this degraded environment. Based on the soil erosion information in 2000 and remote sensing images of Guanling County collected in 2000 and 2007, four grades of karst rocky desertification data in 14 villages of Guanling County were extracted. Impacts of population, affluence, and other human forces on karst rocky desertification were analyzed using STIRPAT model. The results show that:1) Factors of population and affluence had strong influence on karst rocky desertification. In the STIRPAT model analysis, the population and affluence coefficients were positive, indicating that the increase in population and affluence would lead to more serious desertification. 2) Factors of farmer correlated with karst rocky desertification negatively, especially the way of viewing the relationship between people and nature, and the level of knowledge about rocky desertification. Government behavior was not a significant factor in this analysis. 3) The findings provide evidence that STIRPAT model can be used to analyze the relationship between human driving forces and rocky desertification.

The heterogeneous influence of economic growth on environmental pollution: evidence from municipal data of China

This study aims to investigate the influence of rapid economic development on pollution at the municipal level in China.It constructs a Stochastic Impacts by Regression on Population,Affluence and Technology model(STIRPAT model) and uses comprehensive municipal data on industrial pollution and economic performance.The dataset contains 290 cities from2003 to 2016 as a sample for the panel data analysis.The study further separates the cities into two groups by their levels of economic development for heterogeneity analysis.It reveals that a low level of economic development would aggravate environmental pollution,and when the economy reaches a high level,this economic development will improve environmental quality.We also find that the relationships between foreign direct investment and industrial dust and sulfur dioxide(SO_2) discharge are significant,while the relationship between economic growth and effluent emission is not.The more developed subsample cities present an inverted U-shaped curve between industrial pollutant emission,GDP per capita,and foreign direct investment,while the less developed subsamples show no such relationship.Since the shape of these curves differs among regions,their turning points vary accordingly.Based on this finding,this study suggests that the governments of more developed cities should balance environmental pollution and economic development by enhancing environmental regulations and adjusting industrial structure.

Analysis on the Influencing Factors of Low-carbon Economy and Its Mitigation Countermeasures in Sichuan Province

[Objective]The study aimed to analyze the influencing factors of low-carbon economy and its mitigation countermeasures in Sichuan Province.[Method]Taking Sichuan Province as an example,an extended STIRPAT model was established firstly,then the impacts of population,economy and technology on carbon emissions from 2000 to 2009 were analyzed econometrically by using the principal component analysis method.Finally,some corresponding countermeasures to reduce carbon dioxide emissions were put forward.[Result]At present,population scale had the greatest influence on carbon emissions in Sichuan Province,then energy consumption per industrial added value and the proportion of industrial added value to GDP.In addition,the influence of population scale on carbon emissions was still greater than that of population structure,and technical factor also has certain explanatory power on carbon emissions.Some countermeasures,like controlling population growth,advocating low-carbon life style and consumption model,paying more attention to the strategic adjustment of industrial structure to gradually reduce the proportion of high-carbon industries,encouraging energy consumption and emissions reduction plus scientific and technological innovation in a new energy technology filed,could be adopted to reduce carbon dioxide emissions,so as to adjust to the development of low-carbon economy in Sichuan Province.[Conclusion]The research could provide references for the establishment of policies for reducing carbon emissions.

Impacts of Migration on Urban Environmental Pollutant Emissions in China: A Comparative Perspective

In recent years,researchers have devoted considerable attention to identifying the causes of urban environmental pollution.To determine whether migrant populations significantly affect urban environments,we examined the relationship between urban environmental pollutant emissions and migrant populations at the prefectural level using data obtained for 90 Chinese cities evidencing net in-migration.By dividing the permanent populations of these cities into natives and migrants in relation to the population structure,we constructed an improved Stochastic Impacts by Regression on Population,Affluence and Technology model(STIRPAT)that included not only environmental pollutant emission variables but also variables on the cities’attributes.We subsequently conducted detailed analyses of the results of the models to assess the impacts of natives and migrants on environmental pollutant emissions.The main findings of our study were as follows:1)Migrant populations have significant impacts on environmental emissions both in terms of their size and concentration.Specifically,migrant populations have negative impacts on Air Quality Index(AQI)as well as PM2.5 emissions and positive impacts on emissions of NO2 and CO2.2)The impacts of migrant populations on urban environmental pollutant emissions were 8 to 30 times weaker than that of local populations.3)Urban environmental pollutant emissions in different cities differ significantly according to variations in the industrial structures,public transportation facilities,and population densities.

Clean-energy utilization technology in the transformation of existing urban residences in China

Clean-energy substitution technology for existing residential buildings in cities is an inevitable choice for sustainable development and low-carbon ecological city construction.In this paper,the current status of energy-saving renovation and renewable-energy applications for existing residential buildings in various cities in China was summarized by using statistical methods.The geographical distribution of clean-energy power generation in primary energy production in China was explored in depth.According to different climatic divisions for existing urban residences,clean-energy production and consumption were analyzed and predicted based on the STIRPAT model.The results show that the energy consumption of urban residential buildings in 2016 increased by 43.6%compared with 2009,and the percentage of clean energy also increased from 7.9%to 13.4%.Different climatic regions have different advantages regarding clean energy:nuclear power generation leads in the region that experiences hot summers and warm winters,whereas wind and solar power generation lead in the cold and severely cold regions.The present results provide basic data support for the planning and implementation of clean-energy upgrading and transformation systems in existing urban residences in China.

Quantitative analysis of the impact factors of conventional energy carbon emissions in Kazakhstan based on LMDI decomposition and STIRPAT model

Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO_2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO2 emissions, which produced 110.86 Mt CO2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused –110.90 Mt CO2 emissions with a contribution rate of –43.94%, followed by the energy carbon structure effect resulting in –18.76 Mt CO2 emissions with a contribution rate of –7.43%. In order to provide an in-depth examination of the change response between energy-related CO2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO_2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.

Research on influencing factors of urban building carbon emissions based on STIRPAT model——taking Suzhou as an example

The research on the influencing factors of carbon emissions from urban buildings is of great significance for the reduction of carbon in the urban building sector and even the realization of the city’s the carbon peak and neutrality goals.In this paper,combined with the ridge regression method,the STIRPAT model is used to establish a new model for influencing factors of building carbon emissions in Suzhou,and the factors such as urbanization rate,the number of permanent residents,per capita construction and tertiary industry added value,and per capita disposable income are analyzed.The analysis results show that the urbanization rate is the primary driving factor for building carbon emissions in Suzhou,followed by the number of permanent residents,then the added value of the per capita construction industry and tertiary industry,and finally the per capita disposable income.The conclusions of this paper indicate that industrialization and urbanization have strongly promoted the growth of building carbon emissions in Suzhou.In the future,with the continuous development of industrialization and urbanization and the increase of population,Suzhou City can rationally plan urban development boundaries to promote green and low-carbon transformation and development in the field of urban and rural construction,improve residents’low-carbon awareness,and advocate green and low-carbon behavior of residents to reduce building carbon emissions.

Industrial Carbon Reduction Potential Measurement and Scenario Prediction in Shaanxi Province

Promoting industrial carbon reduction is an inevitable step for achieving the Chinese carbon peak and neutrality targets.Based on the industrial energy consumption data of Shaanxi Province from 2011 to 2020,this study uses the IPCC calculation method to calculate the industrial carbon emissions in Shaanxi Province.The prediction model for industrial carbon emissions in Shaanxi Province was constructed based on the STIRPAT model from three aspects:population,economy,and technology.By setting three scenario models,the industrial carbon emissions from 2021 to 2035 and the time to achieve peak carbon neutrality were then predicted.The results show that the industry in Shaanxi Province cannot achieve a carbon peak under the baseline scenario,although it can achieve carbon peaking in 2030 under a low-carbon scenario or in 2025 under an enhanced low-carbon scenario.The predicted carbon peak values are 209.11 million t and 188.36 million t,respectively.Based on the results of this study,four policy recommendations are proposed:(1)strengthen publicity and education efforts to increase public participation in energy conservation and emission reduction;(2)promote the green transformation of industry and develop a green economy,including the active development of energy-saving and emission reduction technologies;(3)accelerate the implementation of industrial carbon reduction;and(4)promote the development and utilization of clean energy and increase efforts to adjust the energy structure.

Changes in the Energy Consumption Carbon Footprint for Gansu Province

Energy consumption is one of the main human activities driving global climate change, and therefore research on the carbon footprint of energy consumption is of great significance. In this paper, concepts and methods relating to the carbon footprint of energy consumption were used to calculate total carbon footprint, carbon footprint of each type of energy, output value of the carbon footprint and its ecological pressure from 1990 to 2009 in Gansu Province, northwestern China. The ridge regression function within the STIRPAT model was applied to study the quantitative relationship between carbon footprint and economic growth and at the same time verify the existence of an Environmental Kuznets Curve. A decoupling index was introduced to further explore the dynamic relationship between economic growth and carbon footprint. We found that the total carbon footprint increased from 0.091 ha per capita in 1990 to 0.191 ha per capita in 2009 and fol owed a lfuctuating rising trend. Coal and oil occupy the dominant position within the carbon footprint composition, while natural gas is of little effect. The output value of the carbon footprint increased from 11 800 CNY per ha in 1990 to 25 100 CNY per ha in 2009, representing an average annual growth rate of 4.1%. The ecological pressure intensity of the carbon footprint increased to 0.24 in 2009, and remains much lower than developed provinces Jiangsu and Shanghai, due to the vast area of woodland in Gansu. Development of a low-carbon economy in Gansu remains hindered by limited energy, a fragile ecological environment and irrational energy structure. Population and GDP per capita growth were the main factors driving the increasing carbon footprint; the impact of population is 3.47 times of that of per capita GDP. Regression analysis and decoupling index analysis have proved the existence of the Environmental Kuznets Curve for economic growth and carbon footprint, but 33 years are required to reach the inlfection point.

Spatiotemporal change of ecological footprint and sustainability analysis for Yangtze Delta Region

In the urbanizing world,the Yangtze Delta Region （YDR） as one of the most developed regions in China,has drawn a lot of the world＇s attention for the remarkable economic development achieved in the past decades.Nevertheless,the rapid economic development was certain to be accompanied by unprecedented consumption and loss of natural resources.Therefore,the analysis of the ecological situation and driving factors of environmental impact was of great significance to serve the local sustainable development decision-making and build a harmonious society.In this paper,the ecological footprint （EF） was taken as the index of the ecological environmental impact.With the help of Geographic Information System （GIS）,we studied the spatiotemporal change of ecological footprint at two scales （region and city） and assessed urban sustainable development ability in YDR.Then we discussed the driving factors that affected the change of ecological footprint by the Stochastic Impacts by Regression on Population,Affluence,and Technology （STIRPAT） model.The results showed that increasing trends of regional ecological footprint during 1998-2008 （1.70-2.53 ha/cap） were accompanied by decreasing ecological capacity （0.31-0.25 ha/cap） but expanding ecological deficit （1.39-2.28 ha/cap）.The distribution pattern of ecological footprint and the degree of sustainable development varied distinctly from city to city in YDR.In 2008,the highest values of ecological footprint （3.85 ha/cap） and the lowest one of sustainable development index （SDI=1） in YDR were both presented in Shanghai.GDP per capita （A） was the most dominant driving force of EF and the classical EKC hypothesis did not exist between A and EF in 1998-2008.Consequently,increasing in ecological supply and reducing in human demand due to technological advances or other factors were one of the most effective ways to promote sustainable development in YDR.Moreover,importance should be attached to change our definition and measurement of prosperity and success.

Does environmental infrastructure investment contribute to emissions reduction?A case of China

Environmental infrastructure investment(EII)is an important environmental policy instrument on responding to greenhouse gas(GHG)emission and air pollution.This paper employs an improved stochastic impact by regression on population,affluence and technology(STRIPAT)model by using panel data from 30 Chinese provinces and municipalities for the period of 2003–2015 to investigate the effect of EII on CO2 emissions,SO2 emissions,and PM2.5 pollution.The results indicate that EII has a positive and significant effect on mitigating CO2 emission.However,the effect of EII on SO2 emission fluctuated although it still contributes to the reduction of PM2.5 pollution through technology innovations.Energy intensity has the largest impact on GHG emissions and air pollution,followed by GDP per capita and industrial structure.In addition,the effect of EII on environmental issues varies in different regions.Such findings suggest that policies on EII should be region-specific so that more appropriate mitigation policies can be raised by considering the local realities.