Decomposing the decoupling of CO2 emissions from economic growth in Cameroon

A ZSG-DEA model with factor constraint cone-based decoupling analysis for household CO2 emissions: a case study on Sichuan province

This study employed an input-output approach to measure household CO2 emissions (HCEs) in Sichuan province, China from 2010 to 2017. A ZSG-DEA model with a factor constraint cone was built, to allocate such emission allowances to provincial subordinate cities and investigate their spatiotemporal heterogeneity. Using Tapio decoupling analysis, this study further examined the nexus between regional economic performance and HCEs. The results indicated that HCEs in Sichuan province initially increased and then decreased, peaking at 69.60 million tons in 2015. The structure of the HCEs changed from coal- to petroleum-dominated from 2010 to 2017. Indirect emissions from consumption-related sectors accounted for 60% of the total HCEs, including food, transportation, communication, and accommodation. The capital city of Chengdu, with the largest economic scale, overwhelmingly dominated the total household CO2 emissions, whereas Panzhihua, a heavy-industry-dominated city, had the highest HCEs  per capita. The Tapio decoupling results suggested that seven cities, led by Chengdu, had strong decoupling statuses, indicating that these cities performed well in the trade-off between economic growth and HCEs reduction. These results indicated that HCEs had a head effect centered on cities with advanced urbanization, but there was a virtuous cycle between CO2 emissions reduction and economic development. Given the spatial and temporal heterogeneity of HCEs, differentiated policymaking on emissions reduction is the key to facilitating green transformations.

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China

As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.

Spatial-temporal characteristics and decoupling effects of China's transportation CO2 emissions

Both the realization of the "double carbon" goal and the low-carbon economy development requires a focus on transportation CO₂ emissions. Calculating Chinese transportation CO₂ emissions and exploring its principles are essential for achieving high-quality development of the transportation industry. Firstly, we use a "top-down" method to assess carbon emissions from transportation operations from 2003 to 2019. Secondly, the study decomposes the influencing factors of transportation CO₂ emissions in China using the log-average weight decomposition method. Thirdly, the Tapio decoupling model is applied to study the decoupling effect of transportation CO₂ emissions in each province of China. The findings suggest that China's transport carbon emissions are growing at an annual rate of roughly 16%. All GDP per capita, transportation energy intensity, and population size increase the growth of transportation CO₂ emissions. Contrastly, energy use per unit of turnover and transportation intensity decrease the growth of transportation CO₂ emissions. There is much variation in China's carbon emission decoupling index from year to year. Policy recommendations are proposed in response to the study of the above findings and the differences in carbon reduction potential among provinces.

Driving Factors and Spatiotemporal Characteristics of CO2 Emissions from Marine Fisheries in China: A Commonly Neglected Carbon-Intensive Sector

The CO₂ emissions from marine fisheries have a significant impact on marine ecology, despite generally being overlooked in studies on global climate change. Few studies have estimated the carbon emissions from marine fisheries while taking into account all pertinent sectors. This study evaluated marine fisheries' CO₂ emissions based on three sectors: marine fishing, mariculture, and the marine aquatic product processing industry. Kernel density estimation and the spatial Durbin model were used to investigate the spatial and temporal characteristics and the key socioeconomic drivers of the CO₂ emissions from marine fisheries in 11 coastal provinces of China from 2005 to 2020. The results are as follows: (1) marine fishing is the sector that produces the most CO₂ emissions; trawling operations generate more CO₂ than all other modes of operation combined; (2) China's marine fisheries' CO₂ emissions show a rising, then declining, trend, with significant differences in coastal provinces; (3) the development of the marine fishery economy and trade have a positive driving effect on CO₂ emissions, the expansion of the tertiary industry does not decrease CO₂, the technical advancement and income growth of fishermen are negatively related to carbon emissions, and the effect of environmental regulation has failed to pass the significance test; (4) the carbon emissions of marine fisheries have significant spatial spillover effects.

The spatial differences of the synergy between CO2 and air pollutant emissions in China's 296 cities

The emissions reduction of CO₂ and air pollutants are the main task in China. The two have the same roots and they interact with each other. However, CO₂ and air pollutants are quite different in space, so it is of great practical significance to explore the spatial differences of their synergy. As PM_2.5 and O₃ are more concerned at present, thus, this paper examined the decoupling of CO₂, PM_2.5 and O₃ from GDP in China's 296 cities using the latest available data from 2015 to 2016. And the spatial differences of synergy among CO₂, PM_2.5 and O₃ were quantitatively analyzed by using spatial autocorrelation analysis and geographically weighted regression model. The results showed that: (1) The cities achieving the three synergy emissions reduction were mainly in the southeast of China. (2) Only 26 cities had achieved the strong decoupling of CO₂, PM_2.5 and O₃ from GDP. (3) The synergy characteristics between CO₂ and PM_2.5, CO₂ and O₃ were different. This paper put forward the policies according to the conclusions.

Grey Correlation Analysis of Transportation Carbon Emissions under the Background of Carbon Peak and Carbon Neutrality

Transportation carbon emission reduction has become an important driving point for China to achieve carbon peak and carbon neutrality. Based on the three-dimensional grey correlation analysis model, taking the five factors affecting transportation carbon emissions, namely, population, GDP, tertiary industry, energy structure and logistics scale, as the research object, the transportation carbon emissions of China’s low-carbon pilot and nonpilot provinces from 2010 to 2019 are calculated based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission accounting method. The time series grey correlation degree and regional grey correlation degree of each influencing factor and traffic carbon emission are obtained using the provincial data, so as to provide policy suggestions for China to achieve the goal of “carbon peak and carbon neutrality” in the field of transportation. The results show that the descending order of the five influencing factors on transportation carbon emissions is: energy structure, logistics scale, population, GDP and tertiary industry. From 2010 to 2019, the time series grey correlation degree between the five influencing factors and transportation carbon emissions shows a fluctuating downward trend, but the impact of demographic factors has become more and more obvious in the past two years; According to the difference of grey correlation degree in different regions, the traffic development of various provinces in China is different, so it is necessary to formulate relevant policies individually.

Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions

Applying the principles of healthy products through agriculture practices has become an important issue due to significant environmental impacts of agrochemicals application. The agrochemicals have been recognized as an essential component of modern agriculture, but they are also an important source of environmental pollution that threatens the human's health and are main sources of carbon emissions. Pesticides and fertilizers application are important in the process of Iran's food production. In Iran, intensifying the agricultural production has led to overuse of chemical fertilizers and pesticides. This work is the first effort to quantify and compare the decoupling index pollution from agricultural sector using Tapio decoupling indicator and predict climate change impacts on this index by using Bayesian network across the whole country of Iran. For this purpose, required annual data of predictor variables for the period of 2008 to 2018 was used to calculate the decoupling index. For projecting climate change impacts on this index by using Bayesian network, monthly mean values of climatic variables were used. While Iranian farmers are criticized for pesticide overuse, these study findings showed that during the period of 2008-2018, decoupling index for pesticides (agricultural pollution by using pesticide) and decoupling index for fertilizer (agricultural pollution by using fertilizer) in the selected provinces fluctuate between RD-SD, SD-SD, SD-SD, and RD-SD. Therefore, the decoupling states show that in most study years, there is a strong decoupling of agricultural growth in selected provinces. This means that in the selected provinces, pollutant emissions of chemical fertilizer and pesticides use for agricultural productions have decreased and it has been well controlled. Therefore, by expansion of agricultural sector, the situation of agricultural pollution in these provinces in most years has not been intensified. Control of agricultural pollution in these provinces has shown a positive and significant impact on public health. In selected provinces, the cleaner agricultural products and application of organic fertilizers have been increased. This study results also showed that the climate change will accelerate increment of pests population and thus pesticides application in different climatic regions.

Decomposition of the decoupling of CO2 emissions from economic growth in Ghana

The study analysed the relationship between CO2 emissions and economic growth in Ghana, specifically by analysing Ghana's decoupling status from 1990 to 2018. The Tapio elasticity method and the logarithmic mean Divisia index decomposition technique were used in the study to find out what causes CO2 emissions in Ghana to change over time. The analysis revealed that CO2 emissions and economic growth have increased over the study period, with economic growth driven mostly by the services and industrial sectors in the last decade. The decoupling index analysis shows that weak decoupling status dominated the period 1990–2018, interspersed with strong decoupling and expansive negative decoupling status. Economic structure and energy intensity, instead, were found to promote the decoupling of CO2 emissions and economic growth. From the decomposition analysis, CO2 emissions in Ghana are driven on the average by economic activities, emission factors, and population growth. To achieve the Sustainable Development Goal 13, the study suggests that policies to cut CO2 emissions should focus on economic activities, factors that affect emissions, and population growth. Also, to decouple CO2 emissions from economic growth, the implementation of policies that change the structure of the economy and energy intensity towards renewable sources should be intensified in Ghana.

Nexus between Human Development Index (HDI) and CO2 emissions in a developing country: decoupling study evidence from Bangladesh

Even if developing nations are entitled to take part in the attempts of decreasing carbon emissions to elude alarming environment change, the consequences of carbon lessening goals in Human Development Index of developing nations continue an issue of dispute. Based on the decoupling relationship and index decomposition, the aim of the study is for determining the nexus between the Human Development Index and CO₂ emissions from the year 1990 to 2018 in Bangladesh. The outcomes exposed that Bangladesh accomplished weak and strong decoupling all through the analysis time. From the index decomposition of different factors point of view, it is revealed that variation in economic activity factor produces a significant amount of CO₂ emissions which is 0.0623 tonnes, and in terms of percentage, it stands for 105%. While energy intensity and economic structure factors act an indiscernible part in the rise of CO₂ emissions and in terms of percentage, it stands for 10.77% and 2.77%, respectively. Contrary, CO₂ emissions' coefficient and energy structure factors are liable for decrease carbon emissions to some extent, and in terms of percentage, it replicates 7.27% and 11.97%, respectively. Based on the outcomes, the paper offers regulations for the energy policymaker of Bangladesh on carbon emissions' alleviation policies and an important decision-making indication to speed up low-carbon growth.

Decoupling of Medical Public-Private Partnership Efficiency and Pollution to Improve Public Health: A Three-Stage DEA Analysis

This study investigates the spatiotemporal evolution of the efficiency of medical public–private partnerships (PPPs) and the decoupling of environmental pollution to promote public health, balanced development, and environmentalism. Based on the 2011–2020 data of medical PPPs in China, the results of a three-stage data envelopment analysis (DEA) and decoupling model show that, firstly, the spatiotemporal evolution of PPP efficiency of China in healthcare has forward periodic twists and turns, and alternating peaks and valleys, which fall into two stages: extensive development, and transformation and upgrading. Secondly, this development is either a type of stable, steady or a surge increase. Thirdly, PPP efficiency and environmental pollution show a weak decoupling state. That is, Northeast China (NEC) and Southwest China (SWC) are in a state of increasing connection, whereas Northwest China (NWC) is in an expanding negative decoupling state. The remaining regions are in a weak decoupling state. This study recommends the mode of ecology-oriented development (EOD) to promote a high-quality, integrated development of PPPs in medicine and healthcare that are especially conducive to a “green economy.” There should be a more coordinated development across regions in China as well.

Decoupling economic development from municipal solid waste generation in China's cities: Assessment and prediction based on Tapio method and EKC models

This study uses the Tapio elastic decoupling analysis method and an empirical model of the environmental Kuznets curve (EKC) to analyze the decoupling between municipal solid waste (MSW) generation and economic development in 285 of China's cities from 2002 to 2017. The decoupling analysis results show that the decoupling states in China's cities generally improved first and then deteriorated in 2002-2017. The proportion of cities experiencing deterioration of decoupling states had increased to 60.00% by 2014-2017, and cities with a higher economic development level generally had more serious deterioration. The empirical test results support the N-shaped EKC hypothesis of MSW, which can explain why the decoupling state changed from improvement to deterioration. The difference of the economic level of sample cities (which determines their position on EKC) can also explain the spatial heterogeneity of the decoupling state and its changes; for example, the proportion of cities that have crossed the inflection point of Tapio decoupling elasticity (the per capita GDP is 13,130.89 yuan) to enter the deterioration stage is highest (100/101) in the economically developed eastern region, and is lowest (66/75) in the economically backward western region of China. In general, the empirical results of EKC can effectively support and explain the results of decoupling analysis.

What drives the decoupling between economic growth and energy-related CO2 emissions in China's agricultural sector?

Many studies have shown that the rapid agricultural mechanization development in China led to substantial energy consumption and CO₂ emission growth. To better explain the mechanism behind the decoupling between economic growth and CO₂ emissions, this paper extends the logarithmic mean Divisia index (LMDI) and production-theoretical decomposition (PDA) considering agricultural decoupling from both structural and technical perspectives. The results reveal that (1) China's agricultural decoupling performance was not ideal. Investment and investment efficiency were the most important factors influencing the decoupling status. The main decoupling obstacle was a higher investment in productivity rather than in energy conservation and carbon reduction. (2) The decoupling status and investment orientation of decoupling efforts among regions were different. Strong negative decoupling statuses frequently occurred in the eastern region, whose main disadvantage was high potential energy intensity. The decoupling status of the central region exhibited expansive features. The decoupling key is to invest more in energy-saving technology rather than in production. The western region changed from weak decoupling to expansive negative decoupling. Both output technology and energy-related factors should be the main investment targets. (3) Weak decoupling and expansive negative decoupling were the most common statuses among provinces. The influence mechanism of drivers exhibited a high spatial heterogeneity at the provincial level. Therefore, the study offered a convincing basis for local governments to formulate low-carbon agricultural development policies by identifying the main decoupling drivers.

Decoupling analysis of the industrial growth and environmental pollution in the Circum-Bohai-Sea region in China

Based on a comprehensive consideration of waste water (WW) and waste gas (WG), the Tapio decoupling model is constructed to explore the decoupling relationship between industrial growth and industrial pollution in the Circum-Bohai-Sea region (CBSR) of China from 2003 to 2016. By dividing 37 sample cities into three sub-regions, we conduct a comparative analysis to describe the spatial-temporal evolution of the decoupling states of industrial growth and environmental pollution. The results show the following: (1) Overall, the industrial WW discharge in 37 key cities has been decoupled from industrial growth, and the industrial development mode is relatively ideal. (2) The decoupling between industrial growth and industrial WW and WG emissions is more ideal in Beijing-Tianjin-Hebei (BTH) than in Midsouthern Liaoning (MSL). (3) There are two nodes for the decoupling between industrial growth and WW and WG in Shandong Peninsula (SDP), and the decoupling state between industrial growth and WG is better than the decoupling state between industrial growth and WW from 2003 to 2016. (4) From 2003 to 2016, the decoupling state between industrial growth and WW and WG in MSL is not ideal. The conclusions show that the decoupling relationship between industrial growth and environmental pollution in the CBSR is still quite variable and unstable; thus, differential treatment measures should be taken. To enhance the effectiveness of these measures, we will further study the main factors affecting the decoupling relationship, and conduct a comparative study in a larger scale.

Energy efficiency's key role in explaining the performance of energy consumption in Andalusia (Spain)

The EU commitment to improve energy efficiency is conditioned not only by the countries but also by the role that the European regions adopt when they develop their own energy policies. Concretely, due to the economic and energy characteristics of the Andalusia, this region conditions the achievement of Spain's goals in terms of energy efficiency. This paper aims to highlight the key role played by energy efficiency, explaining the energy consumption behaviour in Andalusia and in comparison with the Spanish average for the period 2000-2015. The paper analyses this topic through the Logarithmic Mean Divisia Index (LMDI) decomposition method and with a decoupling index analysis. The results show although the energy efficiency measures have been globally effective in terms of reducing the energy intensity between 2000 and 2015, Andalusia still has a higher energy intensity than the Spanish average and more efforts should be made in order to reduce it and to contribute to Spain's energy consumption targets. The main efforts should be focused on the industry and primary sectors. The energy policy recommendation are two. First, to bring the economic situation of Andalusia closer to the Spanish average and therefore to reduce energy intensity and second, to decouple the energy consumption from economic growth, thus contributing to a reduction in CO₂ emissions.

Driving forces and decoupling indicators for carbon emissions from the industrial sector in Egypt, Morocco, Algeria, and Tunisia

North Africa currently accounts for about 40% of Africa's total CO₂ emissions, and the industrial sector is one of the energy-intensive sectors in the region. To this end, special attention should be paid to this region if the African continent's GHG mitigation targets are to be achieved. An extended decomposition approach was combined with the Tapio method to explore the decoupling of CO₂ emissions from industrial growth in North African countries over the period of 1990-2016. The effects of five factors were assessed in the decoupling and the study took into account all fossil fuels used in the industrial sector of this region. Unlike Morocco, Egypt, Tunisia, and Algeria, this study did not consider Libya because of the unavailability of data. Meanwhile, the results showed that: (i) low decoupling was achieved in Tunisia, compared with Morocco and Egypt, where significant decoupling occurred significantly over the study period. (ii) Due to the slowdown in industrial growth, the decoupling analysis did not show satisfactory results in the case of Algeria. (iii) Scale effects contributed to promoting decoupling only in Algeria, while the energy intensity effect played a negative role in decoupling only in Tunisia. (iv) The energy structure effect played an important role in decoupling in Tunisia and Egypt, while the economic structural effect favored decoupling in Tunisia and Morocco alone. An energy policy conducive to the use of more renewable energy is needed to promote decoupling in North African countries.

Investigating effect of R&D investment on decoupling environmental pressure from economic growth in the global top six carbon dioxide emitters

This work is aimed to investigate the effect of research and development (R&D) on reduce in environmental pressures through an empirical analysis of the top six global carbon emitters (the C6: China, USA, India, Russia, Japan, and Germany). This work is valuable toward carbon reduction within C6 countries and the world (C6 emit roughly 60% of the global carbon emissions). Moreover, it is also meaningful for exploring the decoupling of economic development from carbon emissions in other areas (both developing and developed countries). The main findings displayed that the decoupling status in developed countries (i.e., USA, Japan, and Germany) were better and more stable than in developing countries (i.e., China, India, and Russia). Germany performed best among the developed countries, and China performed most stable among the developing countries. The effect of the per capita R&D expenditure was main resistance to decoupling carbon emissions from economic development in C6 countries. However, the energy intensity effect and R&D efficiency effect related to technological progress were the main driving forces for the decoupling process. Consequently, this study proposes that the improvement of technological progress should be prioritized.

Decoupling and mitigation potential analysis of CO2 emissions from Pakistan's transport sector

The transport sector has become one of the major economic, huge fossil fuel energy consumption, and carbon dioxide (CO₂) emitting sector of Pakistan. This study applies the logarithmic mean Divisia index (LMDI) and Tapio's decoupling approach to estimate decoupling state and mitigation potential of CO₂ emissions from the transport sector during 1984-2018. LMDI technique is applied to detect the influencing variables (i.e. carbon coefficient, fuel consumption, total energy consumption, and turn over economy), which oversee CO₂ emissions. The outcomes show that CO₂ coefficient effect is the factor which is decreasing CO₂ emissions while economic growth (EG) effect is the factor which is growing CO₂ emissions. The decoupling index is also applied to influencing factors which reflect the EG factors on CO₂ emissions from the transport sector. The consequences confirm that during 1984-2018, the CO₂ emissions show an expensive coupling with EG. Weak decoupling occurred only in the sub-periods 1999-2003, 2004-2008, and 2009-2013. Similarly, the CO₂ emissions occurred from only three decoupling grades. Furthermore, a mitigation model based on the above impacting variables estimates the mitigation rate of CO₂ emissions and showed that the CO₂ mitigation seemed in 1999-2003, 2004-2008, and 2009-2013. Finally, forecasting outcomes of Tapio decoupling index show a weak decoupling during 2018-2030. Therefore, based on the empirical outcomes, this study puts forward a few policy suggestions to efficiently enhance the decoupling between Pakistan's transport CO₂ emissions and EG.

Carbon emissions in the transportation sector of Yangtze River Economic Belt: decoupling drivers and inequality

Carbon emissions in the transportation sector are of great concern, since they are the third leading contributor to China's carbon emissions. This research examines the decoupling relationship between economic outputs and carbon emissions of 11 provinces in 2000-2016 by focusing on Yangtze River Economic Belt (YREB), which is the densest traffic and economic corridor in China. Although many studies have focused on the decoupling state and its driving forces between economic outputs and carbon emissions, few studies further addressed the microscale analysis for decoupling drivers. This paper reveals the characteristic, inequality contribution ratio, and dynamic evolution of the drivers by integrating Dagum's Gini ratio with kernel density estimation in YREB. Results are as follows: (1) The decoupling states presented weak decoupling state at the whole belt in the majority of the latter observed sub-periods. The decoupling states at the provincial level turned more satisfactory during the four observed sub-periods, especially for Shanghai and Zhejiang. (2) The energy intensity (EI) effect is the predominant driver for promoting the decoupling state in the vast majority of provinces, whereas value added per capita effect is the major role for inhibiting the decoupling state. (3) During the four observed sub-periods, the Gini inequality and transvariation intensity of the EI effect between sub-regions are the main sources of the provincial differences in YREB. The driving force of EI effect is increasing, but the provincial differences are expanding in the upstream and downstream regions by analyzing its dynamic evolution. Understanding the temporal and spatial microscale inequality of the decoupling drivers provides governments with differentiated and forward-looking suggestions towards coordinating regional economic growth and carbon emissions reduction.

The driving forces of energy-related carbon dioxide emissions from South Latin American countries and their impacts on these countries' process of decoupling

An extensive decomposition analysis was used to research the primary driving forces behind energy-related carbon dioxide emissions per capita for the period 1993 to 2017 in a panel of South American countries. Evidence was found that the effect of per capita renewable productivity was challenging and compromised in South Latin American countries. Decoupling changed from a weak state to a strong decoupling state after the Kyoto protocol. When we remove the renewable productivity per capita effect, the results show that the state of strong decoupling was mainly achieved due to a group of economic drivers, with the negative changes in energy intensity, and in the gross domestic investment, proving to be the most significant contributors to decreasing CO₂ emission per capita.

Decoupling of energy-related CO2 emissions from economic growth: a case study of Bangladesh

The objective of this paper is to appraise the decomposition along with decoupling elements that affected the nexus between energy-related CO₂ emissions and the economic development of Bangladesh by applying the logarithmic mean Divisia index and Tapio model. The paper provides an effective policy that will enable Bangladesh to improve its environmental aspect and stimulate sustainable economic development. The analysis comprehends three economic sectors, and the decoupling determinative is fragmented into five elements. The results revealed that Bangladesh achieved weak decoupling throughout the analysis cycles except between the cycles of 1990-1991, 1992-1993, 1995-1996, 1997-1999, 2003-2004, 2012-2013, and 2015-2017 in which a strong decoupling occurred. From the disintegration of various components' frame of reference, it can be seen that change in scale effect causes a substantial increase in carbon emissions and economic structure together, whereas energy intensity plays an imperceptible role in the increase in carbon emissions. On the other hand, the emission is the only element that is responsible for a decrease in carbon emissions. From a comparative sectoral analysis point of view, scale effects are liable for increasing carbon emissions in all three sectors. Hence, precautionary information campaign should be taken to communicate a comprehensive countrywide moderation strategy and appropriate modification engagements for initiating a low-carbon community.

Factor decomposition and decoupling analysis of air pollutant emissions in China's iron and steel industry

With its major influences on economic growth, energy consumption, and environmental quality, the iron and steel (IS) industry plays an important role in achieving green growth of the national economy. It is also the main air pollutant emitter compared with other industries. Therefore, this study first investigates the influencing factors of air pollutant emissions of the IS industry from dimensions of environmental regulation effect, pollutant generation intensity effect, energy structure effect, technological progress effect, and scale effect using the logarithmic mean Divisia index (LMDI) method. Additionally, decoupling effort values are further calculated to obtain the efforts made in different historical stages to achieve decoupling between the growth of the IS industry and its pollutant emissions. Three main conclusions can be summarized based on the empirical analysis of China's IS industry from 2005 to 2015. First, environmental regulation plays a decisive role in mitigating air pollution in the IS industry. Second, environmental regulation and technological progress both exert inhibitory effects on air pollutant emissions, whereas the intensity effect of pollutant generation and scale effect promote emissions to some extent. The role of energy structural effect is unstable, yet the cumulative effect analysis shows that the effect exerts greater impacts on emission reduction during the recent period. Third, decoupling efforts of the industry gradually changed from weak to strong. In specific, the effects of environmental regulation and technological progress both promote decoupling. Conclusions are made, and suggestions are highlighted based on the research findings.

Decomposition and decoupling analysis of energy-related carbon emissions in Turkey

This study focuses on CO₂ emission trends and its decompositions as well as decoupling performance between CO₂ emissions and economic growth of Turkish case for the period of 1990-2016. The drivers of CO₂ emission changes are calculated by using an extended Kaya identity and the well-established logarithmic mean Divisia index (LMDI) method. Decomposition results indicate that economic growth and population effects are the main driving forces in increases in carbon emissions in Turkey throughout the whole period, while other technology-based driving factors' impacts have been rather minimal in reducing the emissions. Decoupling analysis results demonstrate that there is either no decoupling or weak decoupling in most of the years. Moreover, total decoupling effort index suggests that Turkey's performance has been worsened in recent years as we found no decoupling between CO₂ emissions and economic growth over the period of 2013-2016. The overall findings suggest that Turkish economic growth is unsustainable both environmentally and economically. Based on these findings, some policy implications and recommendations are discussed for the possible emission reductions.

Feasibility of peaking carbon emissions of the power sector in China's eight regions: decomposition, decoupling, and prediction analysis

Carbon emissions in the power sector are an important part of China's total carbon emissions and have a significant impact on whether China can achieve the 2030 carbon peak target. Based on the three perspectives of decomposition, decoupling, and prediction, this paper studies the feasibility of carbon emission peaks in eight major regional power sectors in China. First, the generalized Divisia index model (GDIM) is used to decompose the carbon emissions of the eight regional power sectors, and the driving factors and their effects on carbon emissions in the power sector of each region are compared. Then, the decoupling index based on the generalized Divisia index model (GDIM-D) is used to study the decoupling relationship between the carbon emissions of the eight regional power sectors and economic growth. Finally, the carbon emissions and decoupling indices of the power sector from 2017 to 2030 are predicted. The results show the following. First, the gross domestic product (GDP) and output scale are the main factors contributing to the carbon emissions of the eight regional power sectors. The carbon intensity of the power sector in GDP (C/G) and output carbon intensity(C/E) are the main factors that contribute to the reduction. Second, the carbon emissions of the southern coast, the middle Yellow River, and the Southwest peaked in 2013 and have been decoupled from economic growth, while those in the other regions have not peaked or decoupled. Third, if the carbon emissions of the power sector in the Northeast, northern coast, eastern coast, middle Yangtze River, and Northwest reach a peak in 2030, they will face many emission reduction pressures. This paper provides a reference for studying the carbon emissions of China's regional power sectors and their relationship with economic growth and has important implications for peak carbon emissions at the national level.

Regional carbon emission evolution mechanism and its prediction approach: a case study of Hebei, China

Industrialization and urbanization process has brought both economic development and a series of environmental problems. Hebei Province is a high haze area and one of the key areas of China's "Blue Sky Defense" policy. As carbon emission is one of the most important factors causing haze, it is necessary to screen the influencing factors effectively to make quantitative analysis and predict carbon emissions in different degrees of coordination. Faced with the new situation of Beijing-Tianjin-Hebei coordinated development and more environmental constraints, this paper designs a system for predicting carbon emissions in Hebei Province innovatively using the data during 1990-2016. In the first step, 7 influence factors are determined by "graded screening." Then, this paper improved the traditional support vector machine (SVM) by improved gray wolf optimizer (IGWO), and established IGWO-SVM model. Finally, the carbon emissions in Hebei Province from 2017 to 2025 are predicted under three stages of different cooperative development speed of 7 influence factors by the improved intelligent algorithm model. Based on the forecasting results, this paper put forward some pertinent opinions to provide theoretical basis for formulating relevant policies on carbon emissions in Hebei Province to effectively control carbon emissions from the source.

Investigating the determinants of human development index in Pakistan: an empirical analysis

In this modern era, the importance of information and communication technology (ICT) cannot be ignored for sustainable human development. However, Pakistan has experienced a considerable gap between ICT economic growth and human development. In this regard, this study has modeled the relationship between ICT, economic growth, and the human development index (HDI) considering urbanization, foreign direct investment (FDI), and trade for the period from 1990 to 2014. An autoregressive distributed lag (ARDL) and vector error correction model (VECM) approaches are used to analyze the data. The empirical results reveal that ICT promote human development index. Along with, economic growth has a positive and significant impact on human development. Besides, urbanization, trade, and FDI discourage human development in Pakistan. Finally, bidirectional causality is detected between the aforementioned variables. Recommendations to policymakers regarding the productive role of FDI and trade are provided.

Identification and analysis of driving factors of CO2 emissions from economic growth in Pakistan

This study applied the logarithmic mean Divisia index (LMDI) model to identify and discuss the main drivers of Pakistan's CO₂ emissions over the period 1990-2016. The study examined the effects of five factors based on Pakistan's three main economic sectors while considering the 11 types of fuels consumed in that country. The results showed that the energy structure effect is the greatest driving force of CO₂ emissions in this country, followed by scale effect and economic structure effect. Energy intensity is the main contributor to reducing Pakistan's carbon emissions throughout the study period. A comparative review at the sectoral level shows that the industrial sector for which coal is the main source of energy supply is the one that contributes the most to CO₂ emissions in Pakistan. Alongside this sector is the tertiary sector, where the transport sub-sector imposes rules of conduct based on a growing Pakistani population. Meanwhile, deforestation would be the main cause of CO₂ emissions from the agricultural sector in Pakistan, as energy consumption in this sector remains very low. Improving energy efficiency through the intensification of clean energy is urgently needed if Pakistan's environmental goals are to be achieved.

Decomposition of Cameroon's CO2 emissions from 2007 to 2014: an extended Kaya identity

To effectively combat global warming, an enormous reduction in CO₂ emissions is required. Cameroon, which is currently the largest emitter of CO₂ in the CEMAC subregion, has committed to reducing its greenhouse gas emissions by 32% by 2035. However, previous studies in Cameroon have only addressed the relationship between economic growth, energy consumption, and CO₂ emissions without estimating all causal relationships at the same time. Moreover, no study has yet decomposed this country's CO₂ emissions to date. To fill these research gaps and further assess the determinants of these CO₂ emissions, an extended Kaya identity and the Logarithm Mean Divisia Index (LMDI I) have been applied in this paper to identify, quantify, and explain the main drivers of Cameroon's CO₂ emissions from 2007 to 2014. Seven effects were measured and the main findings show that carbon intensity and the emission factor increased by 0.57% and 107.50% respectively. Regarding contributions to the increase of CO₂ emissions, the population effect was the most positive followed by the activity effect, whereas the energy intensity, the substitution of fossil fuels and the penetration of renewable energies have contributed to reduce the CO₂ emission. To enable Cameroon to not only achieve the goals of its vision but also develop a low-carbon economy, this paper provides some proposed avenues that should be considered by policymakers.