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1
Ilyasov, R. X., V. A. Plotnikov, and G. V. Fedotova. "TRENDS IN THE CARBON INTENSITY OF RUSSIA'S GDP: SPLINE ANALYSIS." BULLETIN OF CHECHEN STATE PEDAGOGICAL UNIVERSITY Series 1. Humane and Social Sciences 48, no. 4 (2024): 183–91. https://doi.org/10.54351/25876074-2024-4-48-183.
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The global transition to a low-carbon economy is ensured by an increase in the share of environmentally friendly energy in the consumption structure and the introduction of modern energy-efficient production technologies. Many countries of the world have been able to reduce the carbon intensity of GDP in recent years, but in absolute terms global carbon dioxide emissions are not decreasing – the positive effect of reducing carbon intensity is offset by GDP growth. The dynamics of Russia's GDP production, despite the general growth trend, has shown a decline in some periods of time, for example, under the influence of crises. Since the beginning of 2000, the Russian economy has been affected by three crises, which led to fluctuations in GDP growth and carbon dioxide emissions. The analysis of the impact of crises on trends in carbon intensity of GDP is relevant. The purpose of the study is to analyze changes in the carbon intensity of Russia's GDP occurring in conditions of economic instability. Obviously, classical econometric methods are not sensitive enough for dynamic analysis of trends affected by crises. A new method for economics is becoming relevant, which refuses to distort empirical data by smoothing. Its conceptual basis is the interpolation of data by cubic splines, which preserves all changes in economic dynamics without errors. The accuracy of the constructed model allows us to turn to the dynamic analysis of trends – fluctuations in the growth rate in crisis conditions. The method is based on the method of analytical description of velocity, which is natural in mathematics – differentiation of the model constructed by spline interpolation. In the article, changes in the carbon intensity of Russia's GDP that occurred under the influence of crises are revealed using velocity curves.
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Lukács, Bence, and Árpád Tóth. "Corporate carbon intensity and GDP contribution comparison across 4 countries." Journal of Infrastructure, Policy and Development 9, no. 1 (2025): 10615. https://doi.org/10.24294/jipd10615.
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The paper analyzes the corporate carbon emissions and GDP contributions of the top ten companies by turnover for 2020–2023 in Germany, South Korea, China and the United Kingdom. Focusing on Scope 1, 2, and 3, the study explores the contribution of these companies to carbon intensity across different sectors and economies. The analysis shows that there are significant gaps in carbon efficiency, with the UK’s and Germany’s firms emitting the lowest emissions per unit of GDP contribution, followed by China and South Korea. Additionally, the study further examines the impact of Economic Policy Uncertainty on both firm carbon intensity and economic productivity. While EPU is positively associated with GDP contributions, its impact on emissions is nuanced. Firms apparently respond to policy uncertainty by increasing energy efficiency in direct (Scope 1) and energy-related (Scope 2) emissions but find it more difficult to manage supply chain emissions (Scope 3) in that case. The results point out the critical role of comprehensive ESG reporting frameworks in enhancing transparency and addressing Scope 3 emissions, which remain the largest and most volatile component of corporate carbon footprints. The paper then emphasizes the importance of standardized ESG reporting and bespoke policy intervention for promoting sustainability, especially in carbon-intensive industries. This research contributes to the understanding of how industrial and policy frameworks affect carbon efficiency and economic growth in different national contexts.
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Song, Zhaoxuan, Tingting Zhu, Shihan Yang, and Huiqin Zong. "Prediction and Analysis of Carbon Emissions under Specific Regional Scenarios in Anhui Province based on the STIRPAT Model." Journal of Innovation and Development 3, no. 2 (2023): 41–45. http://dx.doi.org/10.54097/jid.v3i2.9147.
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In order to achieve the goal of reaching carbon peak by 2030, the STIRPAT model is used to predict carbon emissions under three simulation scenarios: baseline, optimization, and strict control of carbon emissions. Taking Anhui Province as an example, fully considering the impact of factors such as population, per capita GDP, carbon emission intensity, energy consumption intensity, energy structure, and industrial structure on carbon emissions, ridge regression and partial least squares regression were conducted respectively. Finally, the partial least squares regression method with a lower average error rate was selected to predict the model coefficients. The results show that all three model scenarios can achieve the carbon peak target by 2030, and the factors that have the greatest impact on carbon emissions are carbon emission intensity, energy consumption intensity, and per capita GDP.
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Meng, Zhaosu, Huan Wang, and Baona Wang. "Empirical Analysis of Carbon Emission Accounting and Influencing Factors of Energy Consumption in China." International Journal of Environmental Research and Public Health 15, no. 11 (2018): 2467. http://dx.doi.org/10.3390/ijerph15112467.
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China is confronting great pressure to reduce carbon emissions. This study focuses on the driving factors of carbon emissions in China using the Logarithmic Mean Divisia Index (LMDI) method. Seven economic factors, including gross domestic product (GDP), investment intensity, research and development (R&D) intensity, energy intensity, research and development (R&D) efficiency, energy structure and province structure are selected and the decomposition model of influencing factors of carbon emissions in China is constructed from a sectoral perspective. The influence of various economic factors on carbon emissions is analyzed quantitatively. Results show that the R&D intensity and energy intensity are the main factors inhibiting the growth of carbon emissions. GDP and investment intensity are the major factors promoting the growth of carbon emissions. The contribution of R&D efficiency to carbon emissions is decreasing. The impacts of energy structure and province structure on carbon emissions are ambiguous through time. Finally, some policy suggestions for strengthening the management of carbon emissions and carbon emission reduction are proposed.
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Mao, Wenqing. "Analysis of influencing factors of carbon emissions in China based on the STIRPAT model." Theoretical and Natural Science 25, no. 1 (2023): 43–50. http://dx.doi.org/10.54254/2753-8818/25/20240898.
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China, as a major economic power, has been increasing its carbon emissions year after year. Effectively controlling carbon emissions and finding suitable and effective methods to reduce emissions have become the main research themes of current research. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model is used in this work to analyze the impact of GDP, population, urbanization, and energy intensity on Chinas carbon emissions from 2003 to 2020. From the output by the SPSS software, it can be illustrated that GDP and energy intensity have more obvious contribution on carbon emission, while urbanization level and population dont. Additionally, as the GDP index increases by a value of one, a 1.220 change will be seen by the carbon emission. Similarly, every one unit change for energy intensity is associated with 0.897 change in carbon emission. Therefore, this paper can consider effective ways to conserve energy and mitigate greenhouse gas emissions from these two aspects, and in this way attain the objective of carbon peaking and carbon neutrality.
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Illarionov, A., and N. Pivovarova. "Economic Consequences of Ratification of the Kyoto Protocol by the Russian Federation." Voprosy Ekonomiki, no. 11 (November 20, 2004): 34–59. http://dx.doi.org/10.32609/0042-8736-2004-11-34-59.
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Carbon dioxide emissions are the inevitable result of hydrocarbon consumption, that is the energy pillar of the modern civilization. These emissions are the function of economic activity and carbon intensity of GDP. With strict natural limits on speed of reduction in the carbon intensity of GDP restraining CO2 emissions means restraining energy consumption and economic activity. Ratification of the Kyoto Protocol by the Russian Federation means that for the first time in the Russian history legal binding limits are put on absolute size of the Russian economy.
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Kyshakevych, Bohdan, Stepan Nastoshyn, Olha Melnyk, Natalia Maksyshko, and Oleksandr Svintsov. "The relationship between economic growth, international trade and energy efficiency in European countries: An Autoregressive Distributed Lag (ARDL) modeling approach." RIVISTA DI STUDI SULLA SOSTENIBILITA' 14, no. 2 (2024): 141–60. https://doi.org/10.3280/riss2024-002009.
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In order to analyze the nature of relationship among time series represented eco-nomic growth, international trade and energy efficiency of 38 European countries for the period from 1995 to 2022 we employed the ARDL model. We identified bidirectional long-term causal relationships between energy intensity and GDP growth rates, as well as between carbon dioxide emissions intensity, GDP per capi-ta and the proportion of exports in GDP. The analysis showed that over the long term, enhancing the energy intensity contributes to an increase in their rate of eco-nomic growth. However, in the short term, energy intensity have a negative impact on the GDP growth. Policymakers can use the paper's findings to design policies that balance benefits of reducing energy intensity with sustainable growth.
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Zhao, Wen Jin, Lun Wang, Zhao Sun, Zhuang Li, and Yu Li. "An IFP Model for Carbon Footprint in Low-Carbon Urban Agglomeration under Uncertainty." Advanced Materials Research 663 (February 2013): 936–40. http://dx.doi.org/10.4028/www.scientific.net/amr.663.936.
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This paper corrected the existing optimization model of low-carbon urban agglomeration using interval fuzzy programming (IFP) method and several constraint conditions are revised according to the 12th Five Year Plan of the urban agglomeration. The case study shows that the carbon footprint of per unit GDP of urban agglomeration was decreased by [21.95, 57.32] (%) and energy intensity was reduced by [25.89, 50.81] (%) compared with those in 2010; meanwhile, the carbon footprint of per unit GDP and energy intensity in the core area was reduced by [18.90, 34.67] (%) and [22.36, 22.76] (%) respectively, compared with those in 2010. The optimized scheme complies with the requirements of the 12th Five-Year Planning Outline of Controlling Greenhouse Gas Emission and the regional planning targets. The corrected model also provided more decision-making space for the sustainable development of low-carbon urban agglomeration.
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Sun, Lili, Hang Yu, Qiang Liu, et al. "Identifying the Key Driving Factors of Carbon Emissions in ‘Belt and Road Initiative’ Countries." Sustainability 14, no. 15 (2022): 9104. http://dx.doi.org/10.3390/su14159104.
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The ‘Belt and Road Initiative’ (B&R) countries play a key role in mitigating global carbon emissions, but their driving factors behind carbon emissions remain unclear. This paper aimed to identify the key driving factors (KDFs) of carbon emissions in the B&R countries based on the extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The empirical results showed that: (1) Population and GDP per capita were the KDFs that promoted carbon emission, while energy intensity improvement and renewable energy were the KDFs that inhibited carbon emissions. Urbanization, another KDF, had a dual impact across countries. (2) The KDFs varied across the B&R countries. For the high-income group (HI), population had the greatest impact. It was identified as the KDF promoting carbon emission, while for the other three income groups, GDP per capita, as the dominant factor, was identified as the KDF promoting carbon emission. (3) Moreover, two interesting trends were found, namely, the higher the income, the greater the impact of energy intensity while the lower the impact of GDP per capita. These results could provide guidance for carbon reduction in the B&R countries.
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Tang, Decai, Yan Zhang, and Brandon J. Bethel. "An Analysis of Disparities and Driving Factors of Carbon Emissions in the Yangtze River Economic Belt." Sustainability 11, no. 8 (2019): 2362. http://dx.doi.org/10.3390/su11082362.
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As one of the “three major strategies” for China’s regional development, the Yangtze River Economic Belt (YREB) is under severe pressure to reduce carbon dioxide emissions, this paper analyzes the spatiotemporal disparities, and driving factors of carbon emissions based on energy consumption and related economic development data in the YREB over the 2005–2016 11-year period. Using the Stochastic Impacts Regression on Population, Affluence and Technology (STIRPAT) model, we empirically test the factors affecting YREB carbon emissions and key drivers in various provinces and municipalities. The main findings are as follows. First, per capita GDP, both industrial structure and energy intensity have positive effects on increasing carbon emissions. Second, per capita GDP and energy intensity have the largest impact on the increase of carbon emissions, and the urbanization rate has the largest inhibitory effect on carbon emissions.
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Zhang, Yulei, Tao Xu, and Songqiang Wu. "The Promoting Effect of Green Bonds on Reducing Carbon Emission Intensity Through Energy Structure Transition." Sustainability 16, no. 21 (2024): 9318. http://dx.doi.org/10.3390/su16219318.
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Climate change poses a significant threat to the sustainable development of all countries. The transition to low-carbon energy sources is a crucial strategy for reducing carbon emissions and mitigating climate change. We investigate the mediating role of clean energy consumption (EC) and fossil energy supply (ES) on the promoting of carbon emission intensity per unit of GDP (CO2/GDP) reduction by green bonds (GBs). We develop a mediating model to analyze how GB influences CO2/GDP reduction through EC and ES, utilizing panel data from 13 prefecture-level cities in Jiangsu province spanning the years 2007 to 2021. Additionally, we assess the model’s reliability through endogeneity and robustness tests. We find that GBs contribute to reducing CO2/GDP by facilitating the structural transition of energy supply and consumption. Furthermore, the development of GBs enhance the consumption of clean energy and plays a direct role in advancing the transition in structure of both energy supply and energy consumption. Notably, we observe heterogeneity in the effectiveness of GBs on CO2/GDP reduction across different regions. Therefore, it is imperative for the government to actively promote the development of GBs to achieve sustainable economic growth. Furthermore, both financial and energy policies should be tailored to align with the specific energy structures of various regions.
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Zhang, Yan Dong, and Tao Zhao. "Analysis on Emission Reduction Targets of Carbon Dioxide in China." Advanced Materials Research 734-737 (August 2013): 1891–95. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.1891.
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This paper is to study the possibility of emission reduction targets to be achieved. With Tapio decoupling model, the decoupling relationship of 30 provinces in11th Five-Year Planis studied. Taking the carbon intensity in 2005 (reduction benchmark), the growth rate of GDP and decoupling elasticity value as indicators, all provinces are clustered into five types of regions. Then the carbon intensity of every region in 2020 is predicted. Some useful results are presented. The growth rate of GDP has no direct impact on the realization of emission reduction targets. The higher reduction benchmark does not restrict the success of emission reduction targets. The region with higher reduction benchmark is much easier to achieve in lowering carbon intensity than the region with lower one. Without powerful relevant policy, the region with lower reduction benchmark and higher decoupling elasticity value is difficult in achieving the emission reduction targets.
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Krutilina, A. D., and I. V. Provornaya. "Dependence of the carbon intensity of the economies of the world on environmental factors." Interexpo GEO-Siberia 2, no. 4 (2022): 74–79. http://dx.doi.org/10.33764/2618-981x-2022-2-4-74-79.
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The paper considers the impact of key factors reflecting the country's transition to "green" energy on reducing the carbon intensity of countries. The revealed carbon price was calculated for the first time for some of the countries under consideration, and after constructing the panel data model, the main significant factors were selected. The country's GDP has the greatest impact on carbon intensity, and the carbon price is similar in influence to the share of renewable energy sources in consumption.
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Luo, Zhenxing, Yanwei Zhao, and Jungmin Lim. "The Nonlinear Effect of Population Aging and Socio-economic Conditions on Carbon Emission: An Empirical Analysis of 30 Provinces and Regions in China." Korean Data Analysis Society 25, no. 3 (2023): 883–902. http://dx.doi.org/10.37727/jkdas.2023.25.3.883.
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The central aim of this paper is to provide a comprehensive analysis of the impact of population aging on carbon emissions in China. In order to attain a deeper comprehension of this effect, this study investigates the non-linear relationship between population aging and carbon emissions through empirical analysis of 30 provinces and regions in China from 1997 to 2019 using a panel threshold model. Our primary research findings indicate that the correlation between population aging and carbon emissions demonstrates nonlinearity. Population aging has had an inhibitory effect on carbon emissions; however, as the aging population continues to increase, its effect on suppressing carbon emissions will weaken. Moreover, we have also uncovered non-linear relationships between energy intensity, per capita GDP, industrial structure, and carbon emissions. Notably, despite rapid growth in total fossil energy consumption, energy intensity has shown a decreasing trend in China, which has mitigated the upsurge in carbon emissions attributed to fluctuations in energy intensity. The positive correlation between GDP per capita and carbon emissions is evident, but there is little variation observed across different threshold levels. Finally, the relationship between industrial structure and carbon emissions is considerably intricate, our results show that a U-shaped curvilinear relationship exists between the two variables.
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Wang, Lun, Zhao Sun, Zhuang Li, Wen Jin Zhao, and Yu Li. "An Optimal Model for Carbon Dioxide Emission Control in the Low-Carbon Urban Agglomeration Based on Sustainable Development of Economy, Society and Environment (2): A Case Study." Advanced Materials Research 610-613 (December 2012): 970–74. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.970.
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Based on an urban agglomeration regional planning in 12th Five-Year Planning , selected two indicators of carbon intensity, energy intensity that were required by the regional planning, this paper developed an optimal model of low-carbon urban agglomeration on the base of sustainable development of economy, society and environment. The case study shows that the carbon emission level of urban agglomeration is 1.8×107 tons in 2015, and the carbon intensity was decreased by 19.00% and energy intensity was reduced by 39.17% compared with those in 2010; meanwhile, the carbon intensity and energy intensity in the core area was reduced by 40.00% and 41.86% respectively compared with those in 2010 subject to the conditions of carbon intensity, carbon sink area, energy intensity, GDP and so on. The optimized scheme could not only meet the requirements of carbon intensity decreased by 17.00%, energy intensity reduced by 16.00% in 2015 compared with those in 2010 proposed by 12th Five-Year Planning Outline of Controlling Greenhouse Gas Emission, but also complied with the requirements of carbon intensity decreased by 18.00% and energy intensity reduced by 20.00% of regional planning targets. The established model also provided more decision-making space for the sustainable development of low-carbon urban agglomeration.
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Vivid Amalia Khusna and Deni Kusumawardani. "Decomposition of Carbon Dioxide (CO2) Emissions in ASEAN Based on Kaya Identity." Indonesian Journal of Energy 4, no. 2 (2021): 101–14. http://dx.doi.org/10.33116/ije.v4i2.122.
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ASEAN is a region with high carbon dioxide (CO2) emissions, accompanied by an increase in population, gross domestic product (GDP) and energy consumption. Population, GDP, and energy consumption can be linked to CO2 emissions through an identity equation called the Rich Identity. This research is based on Kaya identity to describe CO2 emissions to calculate the impact of population, economic activity, energy intensity and carbon intensity on CO2 emissions in ASEAN and 8 ASEAN countries (i.e., Indonesia, Malaysia, Singapore, Thailand, Philippines, Vietnam, Myanmar and Brunei Darussalam) from 1990 to 2017. The method used is the Logarithmic Mean Division Index (LMDI). The data used are from the International Energy Agency (IEA) and the World Bank. Four effects measured and main findings showed that population, economic activity and carbon intensity factor increased by 293.02 MtCO2, 790.0 MtCO2, and 195.51 MtCO2, respectively. Meanwhile, energy intensity effect made ASEAN's CO2 emissions decrease by 283.13 MtCO2. Regarding contributions to the increase in CO2 emissions in all ASEAN countries, the population effect increases CO2 emissions in all countries in ASEAN and the economic activity effect is also the same, except in Brunei Darussalam which makes CO2 emissions in this country decreased by 1.07 MtCO2. Meanwhile, the effects of energy and carbon intensity are different. The effect of energy intensity causes CO2 emissions in lower-middle income countries to decrease, while in upper-middle and high-income countries, it increases carbon emissions. In contrast to the effect of carbon intensity, that actually makes CO2 emissions increase in lower-middle income countries and reduces carbon emissions in upper-middle and high-income countries.
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Provornaya, I. V., and A. O. Khaikina. "Stages of development of the coal industry in Russia to identify possible scenarios for its development." Interexpo GEO-Siberia 2, no. 4 (2022): 211–17. http://dx.doi.org/10.33764/2618-981x-2022-2-4-211-217.
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The paper considers the influence of key factors reflecting the transition of the country to "green" energy, on the reduction of the carbon intensity of countries. The identified price of carbon was calculated for the first time for some of the considered countries, and after building a panel data model, the main significant factors were selected. The country's GDP has the greatest impact on carbon intensity, and the price of carbon in terms of influence is similar to the share of renewable energy sources in consumption. Key words: carbon price, greenhouse gases, carbon tax, panel data, climate change, global warming.
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Provornaya, I. V. "Dynamics of the carbon intensity of the economy by macro-regions using the intensity index of energy intensity reduction." Interexpo GEO-Siberia 2, no. 4 (2022): 141–48. http://dx.doi.org/10.33764/2618-981x-2022-2-4-141-148.
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The paper studies the dynamics of the energy intensity of the economies of the countries of the world. It is shown that by 1980 the process of reducing the energy intensity of the economy began only in the developed countries of North America, Europe, the Asia-Pacific region, as well as in China. The intensity index of energy intensity reduction was calculated, which made it possible to identify the features of the dynamics of GDP energy intensity in developed and developing countries. The presence of convergence in the time series of energy intensity and carbon intensity of the economy of all macroregions, with the exception of the Middle East, where divergence is observed, is determined.
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ZHU, Shouxian, Jianhua DING, and Guiyang ZHUANG. "Analysis of Energy Base for Developing a Low Carbon Economy in Guangdong." Chinese Journal of Urban and Environmental Studies 02, no. 01 (2014): 1450007. http://dx.doi.org/10.1142/s2345748114500079.
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On November 26, 2009, China announced to the world that China would reduce the intensity of carbon dioxide emissions per unit of GDP in 2020 by 40% to 45% compared with the level of 2005. This is "a voluntary action" taken by the Chinese government based on national conditions and was a major contribution to the global effort in tackling climate change. As a province with a large population, surging economy, and huge energy consumption, Guangdong has pioneered policy research and has run pilot projects, seeking to upgrade its industries and to explore an innovative path toward sustainable development and a low-carbon economy. Energy is a fundamental element of low-carbon development and is critical for building a low-carbon society. This paper analyzes the energy base and energy consumption patterns of Guangdong province, exploring its energy mix, self-supply ratio, energy intensity per unit GDP, energy consumption elasticity, and energy quotas for products and industries. The paper uses three low-carbon indicators — carbon productivity, energy consumption and carbon emission per capita, and energy intensity — to analyze and compare energy patterns in Guangdong and in China as a whole. Finally, the paper proposes energy demand trends along with a roadmap for low-carbon development for Guangdong. This paper can also serve as a reference for other provinces seeking low-carbon development in China.
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Li, Jiyu. "Carbon Emission Scenario Projections Based on STIRPAT Modeling: Evidence from Anhui." Highlights in Science, Engineering and Technology 72 (December 15, 2023): 912–20. http://dx.doi.org/10.54097/2wm2kx03.
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As a matter of fact, carbon emission is a hot topic in contemporary society. This paper focuses on the carbon emission problem in Anhui Province, and adopts the IPCC method to measure the industrial carbon emission and total carbon emission in each region of Anhui Province, and analyses the decoupling of GDP and carbon emission in each region of the province from 2011 to 2020 based on the Tapio decoupling model. In addition, this study constructs a prediction model for the trend of carbon emission in the industrial sector of Anhui Province by using the STIRPAT model, and sets up three different scenarios for the indicators of the size of the industrial economy, the industrial output value per capita, the energy structure, the energy intensity, and the intensity of the industrial sector's carbon emission in Anhui, including the energy saving, the baseline, and the aggressive scenarios. According to the analysis, weakly decoupled relationship between GDP and carbon emissions in various regions of Anhui Province. In addition, the peak carbon emissions are 459.27 million tons, 493.25 million tons, and 562.48 million tons in 2030, 2035, and 2040 in the three different scenarios, namely, energy conservation, baseline, and aggressive scenarios, respectively.
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Ilyasov, Ruslan Kh., and Vladimir A. Plotnikov. "Macro-Regional Analysis of the Carbon Intensity of the Economy." Administrative Consulting, no. 7 (175) (June 7, 2023): 42–52. https://doi.org/10.22394/1726-1139-2023-7-42-52.
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Achieving the Sustainable Development Goals formulated by the UN is one of the priorities of modern economic policy. At the same time, its implementation should take into account the macro-regional specifics. Without this kind of accounting, due to the emerging imbalances, it will not be possible to ensure the sustainability of development. One of the components of sustainable development is the decarbonization of the economy. The purpose of the study: macro-regional analysis of the mutual impact of energy consumption, carbon dioxide emissions and economic growth trends. At a time when ensuring sustainable economic growth is an important taskof economic policy, improving energy efficiency turns out to be a key factor in reducing carbon emissions. Research methods: dynamics analysis, structure analysis, methods of comparative analysis and generalization. The article studies the dynamics of changes in the carbon intensity of GDP on the example of two countries with the largest economies — the United States and China. The analysis showed that macro-regions with a high level of technological development manage to reduce carbon dioxide emissions more intensively, while ensuring economic growth.
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Roy, Tapan Kumar, and Nityananda Halder. "FACTORS IMPACT ON POPULATION AND ENVIRONMENT IN BANGLADESH AND INDIA." MAN, ENVIRONMENT AND SOCIETY 3, no. 1 (2022): 37–47. http://dx.doi.org/10.47509/mes.2022.v03i01.03.
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Population growth and trends are centrally important to the environment because it helps to determine the environmental impact of human activities. In this study, the World Bank database has been used. Here, carbon dioxide (CO2) emissions, and energy intensity (EI) are considered as environmental indicators. The population indicators are the proportion of the population aged 15-64 years, and the percentage of the urban population. The Gross Domestic Product (GDP) is considered a development indicator in a country. This study tries to identify the association between population environment and development. Correlation analysis has been employed to know association and Path analysis is used to determine the important factors for environmental impacts such as carbon dioxide (CO2) emissions. The result presents that the zero-order correlation exists among energy intensity (EI), the proportion of the population aged 15-64 (P15-64), urbanization (UR), gross domestic product (GDP) per capita (US$), total population (P) ) and carbon dioxide (CO2) emission in Bangladesh and India. It is observed that 8 paths for Bangladesh and 7 paths for India out of each 12 hypothesized paths are found to be statistically significant. In Bangladesh, the total effects of exogenous variables like as energy intensity (X1) and population aged 15-64 (X2) are observed negative direction on carbon dioxide emissions (X6) and the remaining variable like as urbanization (X3) is observed as positive direction on carbon dioxide emissions. However, in India total effects of these two exogenous variables population aged 15-64 (X2) and urbanization (X3) are observed positive direction on carbon dioxide emissions (X6) and the remaining variable like as energy intensity (X1) is observed negative direction on carbon dioxide emissions (X6). The total effects of endogenous variables like as GDP per capita (X4) show a negative direction on carbon dioxide emissions and population (X5) shows a positive direction on carbon dioxide emissions. The study demonstrates that CO2 emission is important for environmental impact in Bangladesh and India. There is a strong association between population, GDP per capita, energy consumption and urbanization and CO2 emission in Bangladesh and India. The factors of CO2 emissions play an important role in environmental degradation. Thus, attention should be focused on using low energy consumption, and proper urbanization, particularly on modern technology which assures fewer uses of CO2 emissions in Bangladesh and India.
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Qin, Jiancheng, Hui Tao, Minjin Zhan, Qamar Munir, Karthikeyan Brindha, and Guijin Mu. "Scenario Analysis of Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China." Sustainability 11, no. 15 (2019): 4220. http://dx.doi.org/10.3390/su11154220.
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The realization of carbon emissions peak is important in the energy base area of China for the sustainable development of the socio-economic sector. The STIRPAT model was employed to analyze the elasticity of influencing factors of carbon emissions during 1990–2010 in the Xinjiang autonomous region, China. The results display that population growth is the key driving factor for carbon emissions, while energy intensity is the key restraining factor. With 1% change in population, gross domestic product (GDP) per capita, energy intensity, energy structure, urbanization level, and industrial structure, the change in carbon emissions was 0.80%, 0.48%, 0.20%, 0.07%, 0.58%, and 0.47%, respectively. Based on the results from regression analysis, scenario analysis was employed in this study, and it was found that Xinjiang would be difficult to realize carbon emissions peak early around 2030. Under the condition of the medium-high change rates in energy intensity, energy structure, industrial structure, and with the low-medium change rates in population, GDP per capita, and urbanization level, Xinjiang will achieve carbon emissions peak at of 626.21, 636.24, 459.53, and 662.25 million tons in the year of 2030, 2030, 2040, and 2040, respectively. At last, under the background of Chinese carbon emissions peak around 2030, this paper puts forward relevant policies and suggestions to the sustainable socio-economic development for the energy base area, Xinjiang autonomous region.
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Shi, Bo, Qiuhui Jiang, Minjun Shi, and Shunsuke Managi. "Exploring the Effects of Carbon Pricing and Carbon Quota Control on the Energy Transition Towards Carbon Neutrality: A Computable General Equilibrium Analysis of the Zhejiang Region of China." Energies 18, no. 5 (2025): 1029. https://doi.org/10.3390/en18051029.
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The pathway towards carbon neutrality in regions with a relatively light industrial structure and scarce renewable energy resources presents a challenge when balancing energy efficiency improvements with the expansion of renewable energy. Therefore, this study investigates the effectiveness of carbon pricing and carbon quota control as regional carbon abatement policies. The findings demonstrate that carbon taxes are less effective than carbon emission quota control in economic growth and carbon abatement due to their weaker impact on energy efficiency enhancement and structural transition in the energy and industrial sectors. Moreover, stricter carbon pricing, determined by carbon emission goals, leads to greater reduction in sectoral carbon intensity but slower GDP growth caused by the accelerated decline of manufacturing and infrastructure industries compared to carbon intensity quota policies. In addition, carbon pricing derived from carbon emission and intensity quota policies increases reliance on domestically imported electricity, which is constrained by the availability of renewable energy resources.
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Dong, Feng, Xinqi Gao, Jingyun Li, Yuanqing Zhang, and Yajie Liu. "Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches." International Journal of Environmental Research and Public Health 15, no. 12 (2018): 2712. http://dx.doi.org/10.3390/ijerph15122712.
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As the world’s top carbon-emitting country, China has placed great emphasis on understanding the driving factors of carbon emissions and developing appropriate emissions reduction policies. Due to the obvious variations in carbon emissions among various industries in China, corresponding policies need to be formulated for different industries. Through data envelopment analysis, this study introduced the Shephard distance function into the logarithmic mean Divisia index (LMDI) for decomposition analysis, built a carbon emissions decomposition model of 23 industries in China during 2003–2015, and analyzed the impact of 10 factors driving carbon emissions. The main results are as follows. (1) Potential gross domestic production (GDP) is a crucial factor for increasing carbon emissions, whereas potential energy intensity and technological advances of carbon emissions have a significant inhibitory effect on carbon emissions; (2) the technological progress of energy usage and the technological advances of GDP output are manifested by inhibiting carbon emissions at the early stage of development and increasing emissions at the later stage; (3) the structure of coal-based energy consumption is difficult to change in the long term, resulting in a weak effect of energy mix on carbon emissions and an increase in carbon emissions due to the potential energy carbon intensity factor.
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Shen, Zijie, and Liguo Xin. "Characterizing Carbon Emissions and the Associations with Socio-Economic Development in Chinese Cities." International Journal of Environmental Research and Public Health 19, no. 21 (2022): 13786. http://dx.doi.org/10.3390/ijerph192113786.
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Reducing carbon emissions in cities is crucial for addressing climate change, while the city-level emissions of different compositions and their relationships with socio-economic features remain largely unknown in China. Here, we explored the city-level emission pattern from the industrial, transportation, and household sectors and the emission intensity, as well as their associations with socio-economic features in China, using the up-to-date (2020) CO2 emissions based on 0.1° grid (10 × 10 km) emission data. The results show that: (1) CO2 emissions from the industrial sector were considerably dominant (78%), followed by indirect (10%), transportation (8%), and household (2%) emissions on the national scale; (2) combining total emissions with emission intensity, high emission–high intensity cities, which are the most noteworthy regions, were concentrated in the North, while low emission–low intensity types mainly occurred in the South-West; (3) cities with a higher GDP tend to emit more CO2, while higher-income cities tend to emit less CO2, especially from the household sector. Cities with a developed economy, as indicated by GDP and income, would have low emissions per GDP, representing a high emission efficiency. Reducing the proportion of the secondary sector of the economy could significantly decrease CO2 emissions, especially for industrial cities. Therefore, the carbon reduction policy in China should focus on the industrial cities in the North with high emission–high intensity performance. Increasing the income and proportion of the tertiary industry and encouraging compact cities can effectively reduce the total emissions during the economic development and urbanization process.
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Song, Jiekun, Qing Song, Dong Zhang, Youyou Lu, and Long Luan. "Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/684796.
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Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI, MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions. The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has the most significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great.
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Wei, Ting, and Lingzhi Li. "Study on the impact of energy consumption structure index on energy intensity." Highlights in Business, Economics and Management 20 (November 30, 2023): 516–24. http://dx.doi.org/10.54097/hbem.v20i.13035.
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The effects of the industrial structure of energy consumption structure and environmental regulations on energy consumption per unit of GDP are explored and empirically tested using the panel regression method based on panel data of 30 provinces and cities in China from 2000 to 2021. It is found that upgrading the energy consumption structure has a significant inhibitory effect on the energy consumption per unit of GDP, and the results are robust. The mechanism test shows that energy intensity is affected by the industrial structure upgrading effect and the environmental protection expenditure effect on energy consumption structure upgrading. Further discussion through the industrial heterogeneity test reveals that for regions with less coal resource endowment or higher GDP, low-carbon upgrading of energy consumption structure can significantly reduce energy intensity. Upgrading the energy consumption structure for the secondary and tertiary industries can significantly reduce energy intensity. In contrast, upgrading the energy consumption structure for the primary industry can increase energy intensity, but the effect is not significant.
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Myskiv, Galyna, and Iryna Pasinovych. "Sustainable Development of European Countries: The Climate Component." Studia Europejskie – Studies in European Affairs 28, no. 2 (2024): 173–91. http://dx.doi.org/10.33067/se.2.2024.9.
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This article provides a cause-and-effect analysis of the impact of carbon emissions on climate change and on the sustainable development of European countries. Despite the consolidated efforts of the world community, the global amount of greenhouse gas emissions is not decreasing, leading to irreversible climate change. The objective of this paper is to explore Europe’s progress towards climate neutrality in the context of ensuring sustainable development and achieving the goals of the European Green Deal. The article aims to establish an econometric relationship between the amount of carbon emissions and the energy intensity of GDP based on statistical data from European countries, and using the example of Ukraine during a full-scale invasion to demonstrate factors that influence greenhouse-gas- emissions growth despite GDP. A significant aspect of this study is an analysis of data from 2012–2022, which indicates that Europe has reduced CO2 emissions and is successfully moving towards climate neutrality. Key efforts of EU countries in preventing climate change and transitioning to renewable energy sources are reflected in the context of the Green Deal. Based on econometric calculations, the direct relationship between the amount of carbon emissions and energy intensity of GDP in European countries was confirmed with a probability of 0.95. The obtained interdependence allows one to predict the amount of CO2 emissions based on known values of energy intensity of GDP during stable economic development. Special attention was paid to Ukraine and the increase in CO2 emissions due to the war. The authors concluded that the war disrupted the relationship between GDP and CO2 emissions, leading to a 23% increase in emissions in 2022. Overcoming the climate crisis and ensuring sustainable development requires a global decarbonisation strategy. However, effective climate policy is impossible without achieving peace.
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Yan, Yan, Ancheng Pan, Chunyou Wu, and Shusen Gui. "Factors Influencing Indirect Carbon Emission of Residential Consumption in China: A Case of Liaoning Province." Sustainability 11, no. 16 (2019): 4414. http://dx.doi.org/10.3390/su11164414.
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Indirect carbon emissions caused by residential consumption has gradually become the key to the formulation of carbon emission reduction policies. In order to analyze the factors that influence the provincial residential indirect carbon emissions in China, comprehensive structural decomposition analysis (SDA) and logarithmic mean Divisia index (LMDI) models are established in this paper. The Liaoning province was selected due to its typical features as a province with higher urbanization rates. The model is based on input–output tables from 2002 to 2012, including those pertaining to the carbon emission coefficient (ΔF), energy intensity effect (ΔE), intermediate demand (ΔL), commodity structure (ΔS), residential consumption structure (ΔU), residential consumption ratio (ΔR), per capita GDP (ΔA) and population size (ΔP). The results show that the consumption of urban residents is the most common and significant section causing the growth of direct and indirect carbon emissions, both of which show an obvious upward trend. Nonmetal mining is the sector experiencing the greatest growth in indirect carbon emissions. The two most influential factors of indirect carbon emissions via the consumption of rural and urban residents are the intermediate demand effect (ΔL) and the per capita GDP effect (ΔA), respectively. Reducing energy intensity and optimizing commodity structures are the most effective ways to reduce indirect carbon emissions.
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Tang, Zhenlian, and Yuliana Solovieva. "Empirical Analysis and Research of Low-Carbon Economy Development Trends in China, Japan and South Korea." Russian and Chinese Studies 8, no. 1 (2024): 37–45. http://dx.doi.org/10.17150/2587-7445.2024.8(1).37-45.
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Carbon emission intensity, being one of the key indicators of the level of economic development and environmental protection in a particular territory, indicates the quantity of carbon dioxide emitted per unit of GDP. Reducing carbon emission intensity is essential for nations like China (PRC), Japan, and South Korea (ROK) to meet low-carbon policy objectives, which is reflected in the strategic plans of these states. A number of variables (energy structure, industrial structure, technological level and manufacturing techniques) have an impact on the intensity of carbon emissions. Basing on the statistics of the PRC, Japan and the ROK for the period 2011–2020 (pre-epidemic period), the authors carry out a regression analysis of the correlation between the factors influencing the low-carbon economies of the three countries. Taking into account the results obtained, the authors propose a list of policies and measures to enhance the development of low-carbon economies in China, Japan and South Korea.
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Chen, Shuyang. "The Urbanisation Impacts on the Policy Effects of the Carbon Tax in China." Sustainability 13, no. 12 (2021): 6749. http://dx.doi.org/10.3390/su13126749.
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In the literature, very few studies have focused on how urbanisation will influence the policy effects of a climate policy even though urbanisation does have profound socioeconomic impacts. This paper has explored the interrelations among the urbanisation, carbon emissions, GDP, and energy consumption in China using the autoregressive distributed lag (ARDL) model. Then, the unit urbanisation impacts are inputted into the policy evaluation framework of the Computable General Equilibrium (CGE) model in 2015–2030. The results show that the urbanisation had a positive impact on the GDP but a negative impact on the carbon emissions in 1980–2014. These impacts were statistically significant, but its impact on the energy consumption was not statistically significant. In 2015–2030, the urbanisation will have negative impacts on the carbon emissions and intensity. It will decrease the GDP and the household welfare under the carbon tax. The urbanisation will increase the average social cost of carbon (ASCC). Hence, the urbanisation will reinforce the policy effects of the carbon tax on the emissions and welfare.
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Shehu, Muhammad. "Does urbanization intensify carbon emissions in Nigeria?" European Journal of Applied Economics 17, no. 2 (2020): 161–77. http://dx.doi.org/10.5937/ejae17-19472.
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This study examines the urbanization and CO2 emissions nexus in Nigeria using the Autoregressive Distributed Lag (ARDL) method to analyze the annual time series data spanning from 1974 to 2015. Findings suggest that urbanization, GDP, energy use, and carbon emissions are strongly and positively correlated, while trade and carbon emissions exhibit a weak and negative correlation. The ARDL result shows a negatively significant short-term and long-term connection between urbanization and carbon emission in the Nigerian economy. In the short-term, GDP, trade and energy use positively affect carbon emission while in the long-term, trade and GDP negatively affect carbon emissions with energy use having a positive impact on carbon emissions. The study, therefore, concludes that urbanization does not cause carbon emission to rise in Nigeria, but energy use does. From the findings, it was recommended that there is a need for the use of energy-saving and environmentally friendly technology to reduce the amount of carbon emission in the economy.
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Singh Vandana Mayanbahadur. "Drivers of Renewable Energy Consumption: The Roles of Economic Growth, Carbon Intensity, and Innovation." International Journal of Scientific Research in Science and Technology 12, no. 3 (2025): 16–25. https://doi.org/10.32628/ijsrst251222709.
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Purpose- Climate change prevention via aggressive objectives for reducing greenhouse gas emissions is now a top focus on the global, regional, and national levels. Increased reliance on renewable energy sources emerged as the answer to achieving ecumenical energy security and sustainability through carbon neutrality within the practical framework for ecumenical climate action. This would enable countries to reduce energy imports and reduce the use of fossil fuels. Methodology- This study uses an empirical analysis of a large panel of countries and five income-predicted subpanels over the 1995-2019 period to examine the heterogeneous effects of relevant economic and environmental driving factors for renewable energy consumption (REC) that emerge from current policy objectives (GDP per capita, carbon intensity, and research and development). Findings- At the global level, CO2 intensity has a moderating influence on REC, and this link is stronger for low-income and very high-income nations. Additionally, when GDP per capita exceeds the 5000 USD mark, it encourages the use of renewable energy, however in nations with very high incomes, research and development play a significant role in increasing the consumption of renewable energy. Therefore, in order to create effective and consistent regulations, policymakers must take into account the variability of the REC drivers. Originality-Effect of Renewable Energy Consumption, i.e., non- financial indicator, on GDP per capita which is financial indicator.
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Thuan, Nguyen, and Dang Bac Hai. "The impact of energy consumption on Carbon Intensity of Human Well-Being (CIWB)." ECONOMICS AND BUSINESS ADMINISTRATION 11, no. 1 (2021): 19–28. http://dx.doi.org/10.46223/hcmcoujs.econ.en.11.1.1360.2021.
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A key concern when constructing sustainable development policy is reducing the negative impact on environmental systems and maximizing human welfare. In this study, we assess how energy consumption effected on Carbon intensity of human well-being (CIWB). Using two-way fixed effects in panel regression, this relationship has been investigated during 2000-2018 for 9 lower middle-income countries including Algeria, Bangladesh, Egypt, India, Morocco, Pakistan, Philippines, Uzbekistan and Vietnam, while adding GDP and FDI per capita as control variables. The study reveals that the use of energy for economic development is ineffective and inconsistent with the overview of sustainable development due to the result of increasing CIWB. However, the sign of negative coefficients of GDP and FDI per capita in control variables have given the striking findings that these factors will be helpful for lower middle - income countries to pursue sustainable development by reducing CIWB.
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Wen, Lei, and Linlin Huang. "Analysis of influencing factors of Chinese provincial carbon emissions based on projection pursuit model and Markov transfer matrix." International Journal of Climate Change Strategies and Management 11, no. 3 (2019): 406–23. http://dx.doi.org/10.1108/ijccsm-05-2017-0116.
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Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.
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Zheng, Xiaoqi, Yonglong Lu, Jingjing Yuan, et al. "Drivers of change in China’s energy-related CO2emissions." Proceedings of the National Academy of Sciences 117, no. 1 (2019): 29–36. http://dx.doi.org/10.1073/pnas.1908513117.
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CO2emissions are of global concern because of climate change. China has become the largest CO2emitter in the world and presently accounts for 30% of global emissions. Here, we analyze the major drivers of energy-related CO2emissions in China from 1978 when the reform and opening-up policy was launched. We find that 1) there has been a 6-fold increase in energy-related CO2emissions, which was driven primarily (176%) by economic growth followed by population growth (16%), while the effects of energy intensity (−79%) and carbon intensity (−13%) slowed the growth of carbon emissions over most of this period; 2) energy-related CO2emissions are positively related to per capita gross domestic product (GDP), population growth rate, carbon intensity, and energy intensity; and 3) a portfolio of command-and-control policies affecting the drivers has altered the total emission trend. However, given the major role of China in global climate change mitigation, significant future reductions in China’s CO2emissions will require transformation toward low-carbon energy systems.
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DODA, BARAN. "TALES FROM THE TAILS: SECTOR-LEVEL CARBON INTENSITY DISTRIBUTION." Climate Change Economics 09, no. 04 (2018): 1850011. http://dx.doi.org/10.1142/s2010007818500112.
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The level of GDP, its sector composition and the carbon intensity of individual sectors together determine a country’s emissions. To evaluate the contribution of changes in each determinant, I construct counterfactual emissions scenarios in a sample consisting of 34 sectors in 37 countries over 1995–2009. I compare these scenarios quantitatively using a novel metric, namely the relative cumulative emissions. I find that the composition of output and the carbon intensity of sectors individually or jointly constrained emissions in a large majority of countries. This motivates an analysis of high- and low-carbon intensity sectors, denoted HCI and LCI, where emissions and value-added tend to be concentrated, respectively. I document the cross-country variation in HCI sectors’ carbon intensity and show it declines over time largely due to improvements in developing countries. HCI sectors tend to account for a smaller share of employment; be more capital intensive; and employ a workforce with a lower average skill level. Employment declined in HCI sectors and increased in LCI sectors with its composition shifting towards high-skilled workers in both. Capital intensity growth was faster but multifactor productivity growth was slower in HCI sectors.
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Boateng, Forster Kwame. "Effects of Economic Growth, Trade Openness, and Urbanization on Carbon Dioxide Emissions in Ghana, 1960 to 2014." Applied Economics and Finance 7, no. 2 (2020): 9. http://dx.doi.org/10.11114/aef.v7i2.4710.
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This paper examines the effects of per capita gross domestic product (GDP), trade openness, and urbanization on the total carbon dioxide emissions of Ghana using time-series annual data from 1960 to 2014. The 55-year period, from 1960 to 2014, covered economic transformation of Ghana from a low-income agrarian country to a lower-middle income country. The analysis used the autoregressive distributed lag method of co-integration. The results showed that per capita GDP, trade openness, and urbanization all significantly influenced both long-run and short-run levels of carbon dioxide emissions in Ghana. However, increased trade openness led to reduced total emissions, while rising per capita GDP and increased urbanization both increased total emissions albeit at different intensity levels.
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Ogede, Jimoh S., and Hammed O. Tiamiyu. "Does Financial Inclusion Moderate CO2 Emissions in Sub-Saharan Africa? Evidence From Panel Data Analysis." Studia Universitatis „Vasile Goldis” Arad – Economics Series 33, no. 3 (2023): 21–36. http://dx.doi.org/10.2478/sues-2023-0012.
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Abstract The threat posed by climate change has become a reality in the public sphere. This research looks at how financial inclusion affects carbon dioxide emissions in Sub-Saharan Africa (SSA) countries from 2004 to 2017. The panel autoregressive distributed lag and panel granger causality approaches are used to determine if financial inclusion reduces CO2 emissions in Sub-Saharan African countries. The PARDL results demonstrated that, over time, financial inclusion, GDP per capita, industrialization, and trade openness have a substantial beneficial influence on carbon emissions in SSA countries. The result suggests that these considered variables contribute significantly to CO2 emissions while urbanization and energy intensity reduce CO2 emissions in SSA. Financial inclusion and other control variables have no significant impacts on carbon emission in SSA in the short run. The findings of the granger causality test further confirm the direction of causality, revealing that financial inclusion, GDP per capita, industrialization, energy intensity, and trade openness, granger cause carbon emission in SSA countries. Meanwhile, carbon emission does not granger cause any of the considered factors. The study concludes that financial inclusion increases carbon emission in SSA countries, given the poor state of financial inclusion. Our findings advocate for a policy framework that would focus efforts on connecting financial inclusion measures with environmental legislation across SSA nations.
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Zanizdra, Mariya. "Carbon intensity of the Ukrainian industry: current state and foresight." Economy of Industry 1, no. 97 (2022): 61–88. http://dx.doi.org/10.15407/econindustry2022.01.061.
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As part of the current carbon intensity assessment and foresight of the prospects for the decarbonization of the Ukrainian industry, the most carbon-intensive (initially creating a significant carbon footprint) and carbon-vulnerable (showing the highest growth rates of carbon intensity over time) industrial sectors were identified. It is substantiated that the processing industry, agriculture, transport and energy, which have both of the above negative characteristics, are of the greatest competitive importance for Ukrainian GDP and are the most promising sectors for priority development. It is defined that for the period 1990-2020 the carbon intensity of Ukrainian GDP exceeds the global and European average levels, despite certain favorable trends in reducing the greenhouse gas emissions in recent years. As of 2022 the carbon footprint of the Ukrainian industry does not exceed the established quota. However, if current trends continue, it may be exhausted by 2040. At the same time, slow progress in the decarbonization of the Ukrainian energy sector, downward dynamics of the value added in industry, a weak motivating role of environmental taxes and low investment activity of industry do not provide favorable prerequisites for effective decarbonization and digitalization of the industrial complex. The established positive trends in the reduction of carbon intensity are due to destructive phenomena in the long term (deindustrialization of the economy and economic stagnation due to the pandemic) and are temporary in nature, while maintaining the risks of increasing greenhouse gas emissions to pre-crisis levels in case of maintaining the current technological order. According to the basic scenario of the decarbonization of the Ukrainian industry (preservation of current trends and phenomena), an exhaustion of the national quota for greenhouse gas emissions by 2040, further deindustrialization of the economy, an increase in technological gaps with the developed countries of the world and an aggravate in the competitive vulnerability of national exporters are expected. The optimistic scenario assumes successful decarbonization and digitalization of the technological structure of the industrial complex. Its implementation ensures the achievement of "carbon neutrality" of the economy in 2060 and the achievement of other target indicators and qualitative changes planned in the official state strategies for environmental policy and economic development for 2030. However, it requires a significant increase in innovative activity – at the level of results of low-carbon EU-27 leaders, which has taken on heightened commitments to achieve "carbon neutrality". The key condition for the implementation of the optimistic scenario is the participation of Ukraine in international projects to prevent climate change.
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TIMILSINA, GOVINDA R., JING CAO, and MUN HO. "CARBON TAX FOR ACHIEVING CHINA’S NDC: SIMULATIONS OF SOME DESIGN FEATURES USING A CGE MODEL." Climate Change Economics 09, no. 03 (2018): 1850006. http://dx.doi.org/10.1142/s2010007818500069.
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China has set a goal of reducing its CO2 intensity of GDP by 60–65% from the 2005 level in 2030 as its nationally determined contribution (NDC) under the Paris Climate Change Agreement. While the government is considering series of market and nonmarket measures to achieve its target, this study assesses the economic consequences if the target were to meet through a market mechanism, carbon tax. We used a dynamic computable general equilibrium model of China for the analysis. The study shows that the level of carbon tax to achieve the NDC target would be different depending on its design features. An increasing carbon tax that starts at a small rate in 2015 and rises to a level to meet the NDC target in 2030 would cause smaller GDP loss than the carbon tax with a constant rate would do. The GDP loss due to the carbon tax would be smaller when the tax revenue is utilized to cut existing distortionary taxes than when it is transferred to households as a lump-sum rebate.
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Xu, Qian, Junyi Li, Ziqing Lin, et al. "Impact of Economic Agglomeration on Carbon Emission Intensity and Its Spatial Spillover Effect: A Case Study of Guangdong Province, China." Land 14, no. 1 (2025): 197. https://doi.org/10.3390/land14010197.
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Social and economic growth in developing countries has heightened the awareness of environmental challenges, with carbon emissions emerging as a particularly pressing concern. However, the impact of economic development on carbon emission intensity has rarely been considered from the perspective of economic agglomeration, and the relationships and mechanisms between the two remain poorly understood. We analyzed the impact of economic agglomeration on carbon emission intensity and its spatial spillover effect in Guangdong Province, the most economically advantaged province of China, based on a spatial weight matrix generated using geographic proximity, exploratory spatial data analysis (ESDA), and the spatial Durbin model. Between 2000 and 2019, economic agglomeration and carbon emission intensity in Guangdong Province exhibited persistent upward trajectories, whereas between 2016 and 2019, carbon emission intensity gradually approached zero. Further, 80% of the province’s economic output was concentrated in the Pearl River Delta region. Strong spatial autocorrelation was observed between economic agglomeration and carbon emission intensity in the cities, and the economic agglomeration of the province had a parabolic influence on carbon emission intensity. Carbon emission intensity peaked at an economic agglomeration level of 1.2416 × 109 yuan/km2 and then gradually decreased. The spatial spillover effect of the openness degree on carbon emission intensity was positive, while GDP per capita and industrial structure had negative effects. Further, the economic agglomeration effects of Guangdong Province increased the carbon emission intensity of major cities and smaller neighboring cities. The stacking effect of economic agglomeration between cities also affected the carbon emission intensity of neighboring cities in the region. During the period of rapid urban development, industrial development and population agglomeration increased resource and energy consumption, and positive externalities such as the scale effect and knowledge spillover were not well reflected, resulting in greater overall negative environmental externalities relative to positive environmental externalities.
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Zeng, Tianyi, Hong Jin, Xu Gang, Zihang Kang, and Jiayi Luan. "County Economy, Population, Construction Land, and Carbon Intensity in a Shrinkage Scenario." Sustainability 14, no. 17 (2022): 10523. http://dx.doi.org/10.3390/su141710523.
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As the largest ecological background system and basic economic unit in China, counties are of great significance to China’s carbon emission reduction targets. This article conducts theoretical model construction and empirical test research from a contraction perspective, using population and built-up area change as variables and combining indicators of county scale structure in an attempt to find key scale structure elements and representative indicators that affect the carbon emission intensity of counties. By using data from 140 counties in Northeast China during the period of 2015–2020, an empirical study was conducted on population shrinkage clustering, county size structure, and carbon emission intensity. The results show that: (1) population shrinkage significantly increases the carbon intensity of counties, but the contribution of population shrinkage to carbon intensity is scale-heterogeneous, the contribution effect decreases with population size, and the effect on large counties is minimal; (2) population size and industrial structure are the main factors influencing carbon intensity in counties, both have a negative linear elasticity relationship, and GDP per capita is not included in the overall model and is only significant in large counties; (3) the relationship between total construction land and carbon intensity is an inverted U-shaped Kuznets curve, with a critical value of 30 km2, and the total construction land in most counties is below or close to the critical value.
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Albaker, Abdullah, Kashif Raza Abbasi, Akram Masoud Haddad, Magdalena Radulescu, Catalin Manescu, and Georgiana Tatiana Bondac. "Analyzing the Impact of Renewable Energy and Green Innovation on Carbon Emissions in the MENA Region." Energies 16, no. 16 (2023): 6053. http://dx.doi.org/10.3390/en16166053.
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The rising carbon dioxide emissions from the MENA region constitute a severe danger to the environment, public health, and the execution of the United Nations SDGs. Substantial steps are required to solve this problem and maintain the region’s sustainable future. Hence, the current study focused on distinct factors, including renewable energy, energy intensity, green innovation, GDP, and CO2 emissions from 1990 to 2021. The research determines the multifarious variables in various quantiles, including the novel Method of Moments Quantile Regression (MMQR) approach, Fully Modified Ordinary Least Square (FM-OLS), Dynamic Ordinary Least Square (D-OLS) and Driscoll-Kraay Standard Errors (DKS) applied. The findings reveal that renewable energy significantly reduces carbon emissions in all quantiles, while energy intensity, green innovation, and GDP lead to carbon emissions in lower, middle, and upper quantiles. For robust outcome confirmed by FM-OLS, D-OLS, and DKS methods. Also, Granger heterogeneous causality applied that confirmed the bidirectional causality among the variables. The study’s findings imply that authorities should emphasize the emergence of renewable energy and green innovation while adopting energy-efficient technologies to minimize carbon emissions and accomplish SDGs 7, 9, and 13 to secure the MENA region.
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Zhang, Eryu, Xiaoyu He, and Peng Xiao. "Does Smart City Construction Decrease Urban Carbon Emission Intensity? Evidence from a Difference-in-Difference Estimation in China." Sustainability 14, no. 23 (2022): 16097. http://dx.doi.org/10.3390/su142316097.
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Climatic changes and environmental pollution caused by traditional urban development models have increased due to accelerated urbanisation and industrialisation. As a new model of urban development, smart city construction relies on digital technology reform to achieve intelligent urban governance, which is crucial for reducing carbon emission intensity and achieving regional green development. This paper constructs a multi-period DID model based on panel data from 283 cities from 2007 to 2019 to explore the impact of smart city construction on urban carbon emission intensity. This study found that smart city construction decreased urban carbon emissions intensity significantly and decreased carbon emissions per unit GDP in pilot areas by 0.1987 tonnes/10,000 CNY compared to that in non-pilot areas. According to a heterogeneity analysis, the integration of smart city developments could decrease carbon emission intensity in northern China’s cities and resource-based cities significantly but had an insignificant influence on carbon emission intensity in southern China’s cities and non-resource-based cities. The reason for this finding is that northern cities and resource-based cities have a higher carbon emission intensity and enjoy more marginal benefits from smart city construction. Based on an analysis of the influencing mechanisms, smart city construction can decrease urban carbon emission intensity by stimulating green innovation vitality, upgrading industrial structures, and decreasing energy consumption. These research conclusions can provide directions for urban transformation and low-carbon development, as well as a case study and experience for countries that have not yet established smart city construction.
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Yue, Jin Gui, Yuan Jun Yu, and Lin Wu. "Analysis of Energy Consumption Carbon Emissions in Hunan Province Based on Industrial Structure." Applied Mechanics and Materials 694 (November 2014): 528–31. http://dx.doi.org/10.4028/www.scientific.net/amm.694.528.
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Hunan province energy consumption carbon emissions based on the industrial structure was analyzed with carbon emissions factor method in 2000-2012. Results show that Hunan province’s carbon emissions have a rapid growth in 2000-2012. Since 2007 the growth of carbon intensity is slowly, and there is an emergence of signs of decline. Recently the correlation between the growth of GDP and carbon emissions in Hunan Province becomes weakening, but carbon intensity is still higher. Industry occupies a dominant position in the energy consumption carbon emissions. Since 2007 the proportion of industrial carbon emissions is decreased form 79.41% to 72.30% in 2012, there is an obvious decline. Recently, the growth rate of industrial carbon emissions is relative lower. The growth of carbon emissions from the construction industry and the tertiary industry is the most obvious. Relevant policies should be formulated as soon as possible, to promote the level of construction technology, control energy consumption and carbon emissions per unit of output.
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Zhao, Haoran, Zhen Yang, Shunan Wu, et al. "Comprehensive Assessment and Obstacle Analysis on Low-Carbon Development Quality of 30 Provincial Regions in China." Sustainability 17, no. 6 (2025): 2425. https://doi.org/10.3390/su17062425.
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Low-carbon development (LCD) in China has become the critical measure to achieve sustainable development and handle climate change. This investigation evaluates 30 provincial regions’ LCD quality from dimensions of low-carbon (LC) economy, resources utilization, LC environment, and LC society. According to the integrated weights combined subjective weights identified through the best–worst method (BWM) and objective weights attained through the anti-entropy weight (AEW) method, the top five sub-criteria in 2021 were coal consumption relative to total primary energy consumption, industrial sulfur dioxide (SO2) emission, carbon dioxide emissions intensity, industrial dust emission, and forest coverage rate. According to the comprehensive evaluation results obtained through the MARCOS model, Beijing’s comprehensive score is far ahead, and its scores in resource utilization, LC environment, and LC economy are also in a leading position. Moreover, the level of LCD quality shows a gradually reduced pattern from east to west. The obstacle analysis demonstrates that the obstacle factors with high frequency of occurrence include real GDP, energy intensity, coal consumption relative to total primary energy consuming, carbon dioxide emissions intensity, industrial dust emission, industrial SO2 emission, forest coverage rate, and the number of private vehicles. Suggestions are proposed based on the results, including increase infrastructure construction, optimize energy structure and develop renewable energy, protect the ecological environment with intensify efforts, and accelerate industrial transformation and upgrading to optimize industrial structure.
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Ilyasov, R. Kh, and V. A. Plotnikov. "Macro-Regional Analysis of the Carbon Intensity of the Economy." Administrative Consulting, no. 7 (August 2, 2023): 42–52. http://dx.doi.org/10.22394/1726-1139-2023-7-42-52.
Full textAbstract:
Achieving the Sustainable Development Goals formulated by the UN is one of the priorities of modern economic policy. At the same time, its implementation should take into account the macro-regional specifics. Without this kind of accounting, due to the emerging imbalances, it will not be possible to ensure the sustainability of development. One of the components of sustainable development is the decarbonization of the economy. The purpose of the study: macro-regional analysis of the mutual impact of energy consumption, carbon dioxide emissions and economic growth trends. At a time when ensuring sustainable economic growth is an important task of economic policy, improving energy efficiency turns out to be a key factor in reducing carbon emissions. Research methods: dynamics analysis, structure analysis, methods of comparative analysis and generalization. The article studies the dynamics of changes in the carbon intensity of GDP on the example of two countries with the largest economies — the United States and China. The analysis showed that macro-regions with a high level of technological development manage to reduce carbon dioxide emissions more intensively, while ensuring economic growth.
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Yakymchuk, Alina, Simone Maxand, and Anna Lewandowska. "Economic Analysis of Global CO2 Emissions and Energy Consumption Based on the World Kaya Identity." Energies 18, no. 7 (2025): 1661. https://doi.org/10.3390/en18071661.
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This research seeks to elucidate the relationship between economic activities, energy consumption, and CO2 emissions, thereby contributing to a deeper understanding of the economic dimensions of climate change mitigation efforts within the European context, which may be useful for developing policies to mitigate CO2 emissions and promote sustainable development. This study investigates world CO2 emissions and their relation to population growth and finds a strong positive relation based on data from 1969 to 2023. The World Kaya Identity has been applied to understand how changes in the involved factors affect CO2 emissions over time. When studying the more complex relation between the variables by controlling for energy use, GDP, and carbon intensity based on the Kaya Identity, the authors identified an overall long-term coupling of all factors. Considering short-term variations, population growth appears to have an insignificant effect, and carbon intensity appears most influential on CO2 emissions. As a next step, we take a disaggregated view on different country settings, economic sectors, and energy sources to further analyze the role carbon intensity plays for increased CO2 emissions. Here, we lay a special focus on the European perspective. This descriptive analysis lets us draw some general conclusions regarding strategies for reducing the negative impact of CO2 emissions and political efforts for sustainability transformations. This study is important for the current state of science, since a clear economic assessment of the negative effects of carbon dioxide is necessary for planning measures and costs in the ecological sphere, the correct assessment of the impact on the health of the population, the prospective implementation of preventive measures at all levels, and financing measures to reduce the negative effects of carbon dioxide. The authors found a significant positive effect of GDPpc, energy intensity, and carbon intensity on impact and an insignificant effect on the population. Thus, an unexpected increase in the population likely does not have short-term effects on CO2 emissions, and the responses to GDPpc and energy intensity both decrease after some periods, while the shock in carbon intensity shows a significant effect even after 10 years. This is reasonable in the sense that both increases in GDP and energy intensity might be alleviated by technological progress and, thus, only show a short-term positive effect on CO2 emissions. The carbon intensity of energy consumption is more crucial for the long-term change of CO2 emissions. For this reason, we study the decomposition of energy use in more detail by considering descriptive statistics over time and over different sectors and countries.