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Abstract: As world's top two carbon emitters, driver analysis of China and the USA helped the governments to develop policies to cut or slow down carbon emission. Many studies identified the factors affecting carbon emission in China and the USA (emitting more than 40% of the global CO 2 emission), however, few studies considered stratified heterogeneity or the interactions of factors. Here, we adopted the modified Geographical Detector tool to investigate the main drivers of carbon emission from the perspective of stratified heterogeneity. The results of this analysis showed that human economic activities in China were the dominant effect of carbon emission changes, while energy intensity contributed toward controlling the carbon emission in China. Furthermore, population growth was the most significant driving force followed by energy intensity toward controlling the carbon emission of the USA. All these factors are mutually enhancing in changing carbon emissions, while oil share with energy intensity and coal share were more significantly enhanced in China's carbon emission than other interactions. The factors of human activities and energy mix posed a more powerful effect when they mutually enhanced each other to change carbon emission compared to other enhancing interactions. This work represents a pilot scheme for a carbon dioxide emission analysis from the categorical stratified heterogeneity based on statistical methods. [ABSTRACT FROM AUTHOR]

Global climate change caused by carbon emissions poses a severe challenge to human economic and social development. The Chinese government has committed to a series of emission reduction initiatives to achieve carbon intensity targets by actively promoting the green transformation of the industrial sector—the main source of energy consumption and environmental pollution. This transformation has been ongoing for more than five years, and the effects, problems and experiences are worth discussing. Therefore, using province-level panel data for China's industry from 2010 to 2015, we systematically analyze the regional structure and developmental trend of industrial green transformation and empirically investigate its dynamic threshold effects on carbon intensity under different degrees of environmental regulation. The results show that China's industry has gradually undergone a green transformation, which has significantly reduced pollution emissions. However, the process has a large developmental scope due to regional heterogeneity and fluctuation characteristics. Interestingly, the impact of the industrial green transformation on carbon intensity is limited by the "critical mass" of environmental regulations. Paradoxically, weak environmental regulation significantly facilitates a decrease in carbon intensity through industrial green transformation. Once environmental regulation surpasses a critical level, the role of this transformation in CO 2 reduction is weakened, resulting in a failure to decrease carbon intensity. We provide insights into the driving factors that reduce carbon intensity and improve our understanding of the driving forces, paths and policy designs needed to successfully reach carbon intensity targets. [ABSTRACT FROM AUTHOR]

A better understanding of the economy-material-emissions nexus is fundamental in order to reveal the interactions between human development and the natural environment, and more importantly, to design integrated de-carbonization and de-materialization policies. In this paper, we conducted a decoupling analysis of fossil fuel-induced CO 2 emissions and in-use material stocks in infrastructure to economic growth at the provincial level in China and investigated the trilateral causal relationships among the three indicators. The results show that the average elasticity of CO 2 emissions and material stocks to economic growth was smaller than 1, representing a status of relative decoupling. However, in many less developed provinces in central and western China, we observed increasing trends of elasticity in the past three decades, which suggest their economic growth became more tightly linked to CO 2 emissions and the accumulation of material stocks. Granger tests suggest that in the long run there existed a unidirectional causality running from CO 2 emissions, economic growth and urbanization to material stocks. In the short run, a bi-directional causality between CO 2 emissions and economic growth and a unidirectional causality from material stocks to CO 2 emissions were detected. Policy implications for the de-carbonization and de-materialization transition include enhancing renewable energy utilization, upgrading industries to less carbon-intensive ones, developing compact cities, prolonging the lifespan of infrastructure, and strengthening the life-cycle carbon management of infrastructure. [ABSTRACT FROM AUTHOR]

In China, environmental problems associated with excessive energy consumption are escalating with rapid economic development, and the greenhouse effect caused by increased carbon dioxide emissions has risen into the focus of domestic and even international attention. Based on the reduction target China committed in 2015 Paris Climate Agreement, the study proposes a dynamic factorization model to decompose and compare the effect of industrial structure on the reduction of carbon emissions during the five Five-Year Plan (FYP) Periods from 2006 to 2030 at the dimensions of industries and sectors. Results show that China's industrial restructuring exerts a positive impact on reduction of carbon emissions and the impact varies with the ratio of sectors within the economic structure, in the later five years, carbon reductions emissions causing by restructuring in the three major industries and in the secondary industrial sectors account for 28.22% and 4.26% of the national total carbon reductions emissions respectively; in industrial sectors to mitigation was less than in the three major industries in the past two FYP Periods, but will increase in the next three FYP Periods. These findings are significant for the government in formulating environment and economy policy and planning. [ABSTRACT FROM AUTHOR]

China is one of the highest growth economies in the world, and it has experienced a significant rise in energy consumption and greenhouse gas emissions in recent decades. Its economic growth heavily depends on fossil fuels of coal and oil which led to huge amount of greenhouse gas emissions. This study logically analyzes the nexus of CO 2 emissions, economic growth and energy consumption (by decoupling greenhouse gas emission of CO 2 and economic growth) to provide more conclusive evidence on the phenomenon of the environmental Kuznets curve (EKC) over the period 1970–2015. To achieve the objective of consistency in the estimation results, the study applies different estimation techniques such as ARDL (Auto regressive Distributed Lag) model, FMOLS (Fully Modified Ordinary Least Squares, DOLS (Dynamic Ordinary Least Squares), and impulse response and variance decomposition. In addition, ‘the business as usual' inverted U-shaped relationship between GDP per capita and an economy-related CO 2 emission is hypothesized. The result supports the Environmental Kuznets Curve (EKC) hypothesis from different techniques, the turning point hovers around $744665. The estimation result indicates that China EKC turning point shows some inconsistencies when compared to other turning points obtained from different studies. The inconsistency of the EKC turning point is attributed to the sensitivity of the result to different data source, variables selection, different pollutants and scope of the data. Although the EKC turning point of this present research differs from other China EKC turning points, it gives a clear policy roadmap on the pursuance of long run economic growth in favour of environmental quality. Besides, significance of the turning point lies with the need to explore other structural policies such as articulated demographic and energy policies rather than passively waiting for the arrival of the inflexion point. One interesting findings of the analysis is the consistency of the estimation results from the different estimation techniques. [ABSTRACT FROM AUTHOR]

China has the world’s highest levels of carbon dioxide (CO 2 ) emissions, and its adoption of optimal measures to address climate change is extremely important. In this article, we assess the performance of various Chinese policies aimed at addressing climate change in terms of cost effectiveness, cost-benefit efficiency, and their contribution to a reduction in emissions with a new single-region model, DEMETER-CCPE. Our analysis shows that the performance of these Chinese policies strongly depends on the principles and indicators used in the evaluation. When climate-specific cost indicators are considered, the mixed policy performs best in terms of cost reduction. When cost-efficiency indicators are used, a single policy performs better to some extent. From the perspective of evaluating the contribution to emissions reduction, the development of non-fossil-fuel-based energy technologies offers the greatest opportunity for reducing emissions in a mixed-policy scenario. Finally, this article study possible ‘burden’ and ‘free-rider’ scenarios in order to explore the effect of different levels of joint actions between China and the rest of the world (ROW) on a performance evaluation of policies to address climate change. [ABSTRACT FROM AUTHOR]

This study incorporates demand emission sensitivity into a programming model to explore the issue of decoupling the lock-in effect between economic growth and CO 2 emissions through structure adjustment from a final demand perspective. As a demonstration, the proposed methodology is applied to data from China for the year 2012, the most relevant productive linkages and the most sensitive components of final demand in terms of CO 2 emissions were identified. The results indicate that to formulate better carbon mitigation policies, we should pay more attention to the main internal linkages in terms of CO 2 emissions within the economy. [ABSTRACT FROM AUTHOR]

Using the logarithmic mean Divisia index (LMDI) method, we decomposed factors affecting the national carbon intensity (NCI) in China at a multi-regional level. A comparative analysis of regional contributions in different Five-Year Plan (5YP) periods revealed that the energy structure had inhibitory effects on NCI during the 9th and 10th 5YP periods, but had promoting effects during the 11th and start of the 12th 5YP periods. The energy structure had a strong inhibitory impact in eastern, north-eastern, and northern regions of China, but only a slight impact in central, northern, south-western, and north-western regions. Energy intensity is a major factor and had a strong inhibitory effect on NCI except during the 10th 5YP period. Energy intensity had a strong inhibitory effect in eastern and northern regions, but only a slight impact in north-western and southern regions, especially in Hainan, Ningxia, Xinjiang, and Qinghai provinces. Overall, proportional output in China had a promoting effect on NCI, which indicates regional economic development was not coordinated. The combined effects of regional carbon intensity (RCI) and proportional output determined changes in NCI, and RCI had a strong effect on NCI. Regions with high proportional output and large carbon emissions had a great impact on NCI. The total effect in developed areas such as eastern, northern, and north-eastern regions was strongly inhibitory, while the impact was slight in relatively less developed areas such as northwestern and southern regions. Some policy implications arising from our study results are discussed. [ABSTRACT FROM AUTHOR]

Urbanization results in a considerable economic disparity between urban and rural areas in developing countries, which has had a consequent significant impact on CO 2 emissions. To accommodate this directly, this paper presents a modified version of the Kaya Identity formula, U-Kaya, that includes a direct urbanization factor to determine the relationship between energy consumption intensity, population growth, urbanization rates, urban and rural GDP per capita and the volume of carbon emissions, for application in the dynamic forecasting of carbon emissions by Monte Carlo simulation. The method is demonstrated in forecasting China's likely carbon emissions in 2020 by three different modes of urbanization policy: the government-dominant mode, market-dominant mode and a hybrid government market-dominant mode. The results indicate that a higher urbanization rate, energy carbon emission coefficient and energy intensity will lead to increased carbon emissions. Finally, three policy implications are identified, comprising the narrowing of the economic gap between urban and rural areas, adjustments to the energy structure and technical innovation, which will provide a valuable reference for developing countries in their efforts to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

This study estimates how carbon price affects diffusion of low carbon technologies using data collected from 60 iron & steel companies in China. The analysis indicates that the industry is familiar with related key energy saving and low carbon technologies and has made much progress in energy saving, but efforts in carbon management need improving. An average payback time threshold of 4.1 years was found as regards decision in invest in technology. The three target technologies were adopted by companies to different extents. Carbon emissions pricing was found to be ineffective in promoting the scale-up or further adoption of economically advantageous technologies such as dry top pressure recovery turbine (Dry TRT) and energy management centre (EMC) technology. A moderate carbon price for sintering waste heat recovery power generation (Sintering WHR), which has adoption potential in China's iron & steel industry, may generate comparatively significant effects for diffusion. This research sheds light on how diffusion of low carbon technologies would be affected and the consequences thereof on climate policy development for the target sector in China. [ABSTRACT FROM AUTHOR]

This paper specifies a nonlinear threshold cointegration to analyze the nonlinear relationship between carbon intensity and economic growth in China. The tests and estimated results clearly confirm that fast-paced economic growth—that is the growth rate of gross domestic product (GDP) is greater than 9.053%—impede the reduction of carbon intensity because it weakens the negative effects of tertiary industry development and energy efficiency on carbon intensity while it also increases the positive effect of unclean energy mix on carbon intensity. In order to find a moderate range of economic growth rate that is both beneficial for industrial structuring and energy mix transformation, and at the same time, facilitate reduction in carbon intensity, Monte Carlo simulations are employed. The results show when the growth rate of GDP is between 7% and 8.4%, it can promote “structural bonus” of industrial structure and energy consumption structure on carbon intensity, and carbon intensity in 2020 would decline by about 40.60% compared to 2005 level. These conclusions mean it is necessary for China to carefully set its economic growth target through transformation and optimization of its economic structure and energy mix in order to continuously reduce carbon intensity. [ABSTRACT FROM AUTHOR]

As CO 2 emissions differ in households with different characteristics, mitigation policies still address the contributions of these characteristics and their effects on specific households. Using a representative resident survey in urban China, this paper compares how household characteristics differ in their associations with household embedded carbon emissions (HECEs) at different levels by quantile regression; then, this paper analyzes the contributions of these characteristics by Shapley decomposition. The findings include the following: (i) household income is the most important contributor to the difference of HECEs, and its positive effect increases as HECEs rise; (ii) the use of wealth has different effects on HECEs because household housing ownership and deposits contribute little to HECEs, but the positive effects of household car ownership contribute 25.8%; (iii) young people and children will emit more HECEs than adults; the employed emit more than persons who are unemployed or retired; (iv) education increases HECEs overall but mainly at the low quantiles; the negative effect of the marriage increases as HECEs rise; (v) income, car ownership, marital status, size and constitution of households with high HECEs should be addressed to reduce the inequality of HECEs. Indeed, education on the low-carbon concept is another way forward. [ABSTRACT FROM AUTHOR]

Increasing concern about carbon dioxide (CO 2 ) emission reduction demands knowledge about the production structure of an economy. Information on productive linkages yields insight about forward and backward emission effects associated with sectoral inputs and outputs and serves as an essential starting point to identify the optimised industrial structure under the constraint of CO 2 emission. This paper proposes a combined linkage analysis and multi-objective programming approach to identify the key CO 2 emission sectors and the optimised production structure with respect to emission reduction target. As a demonstration, the proposed approach is applied to data from China in 2007. The result shows that to reduce CO 2 emissions from 5707.16 to 5452.12 million tonnes, China needs to change its industrial structure by focussing on industrial groups as defined by linkage characteristics, which would lead to a subsequent GDP decrease of 82.59 billion Yuan. From a policy standpoint, the analytical techniques described in this paper can provide valuable information for planners and decision makers to formulate feasible and practical industrial polices with implications for CO 2 emissions. [ABSTRACT FROM AUTHOR]