Search

Abstract Cities do play significant roles in tackling global environmental changes. Decoupling economic growth from environmental pressures is critical for global sustainable development, however, few studies pay attention to the decoupling analysis at city level. This paper conducts a comparative study of the decoupling performance of economic output from CO 2 emissions between Beijing and Shanghai, the top two cities in China. In this paper, the decoupling trends and the decoupling effects are explored from a sectoral perspective, by combining the Tapio decoupling elasticity and the logarithmic mean Divisia index (LMDI) model. The results show that Beijing and Shanghai both experienced weak decoupling in construction, expansive negative decoupling in transport, expansive coupling in trade, and weak decoupling in others over the period 2000–2015. Moreover, agriculture appeared strong decoupling in Beijing, and recessive decoupling in Shanghai; industry showed strong decoupling in Beijing, and weak decoupling in Shanghai. The decoupling effects showed different magnitudes and patterns in industrial sectors between Beijing and Shanghai. Overall, per-capita GDP and population growth inhibited the decoupling, while energy intensity, industrial share, energy mix accelerated the decoupling process. Highlights • The decoupling of sectoral economic output from carbon emissions in Beijing and Shanghai are investigated. • Beijing's industry enjoyed better decoupling performance than Shanghai's industry. • Transport should be put more emphasis in further decoupling efforts. • Energy intensity is crucial effect accelerating the decoupling process. [ABSTRACT FROM AUTHOR]

The metallurgy industry plays a very important role in China's economic growth. Due to its energy concentration, the energy consumption of the industry accounts for a large proportion of China's total energy consumption, and thus contributes to carbon emissions. Whether the metallurgy industry can successfully reduce emissions is related to the future of China's carbon emissions targets. In this paper, the logarithmic mean Divisia index model is used to analyze the change in China's carbon dioxide emissions from 1991 to 2014. The empirical results show that there are three main factors affecting the carbon dioxide emissions of China's metallurgy industry. The three factors are labor productivity, energy intensity and industry size. In addition, we further analyzed the deep motivation of carbon dioxide emissions in the industry, and calculated the future emission reduction potentials. Lastly, according to the empirical results, the paper proposes policy recommendations for the future emission reduction of China's metallurgy industry. [ABSTRACT FROM AUTHOR]

Ecological sustainability of the Chinese economy is vital for China as well as for the world. By integrating environmental input-output analysis, emergy theory, and a complex network model into one framework, this paper explores Chinese sectoral ecological sustainability with the most recent 2012 input-output table. The results show that there are significant differences in emergy intensities among sectors. The non-metal mining sector is the most emergy-intensive sector at 6.06E+17 sej/10000 Yuan, followed by the metal mining sector. Additionally, output connections have a positive impact on the emergy yield ratio, input connections exert a negative impact on the emergy yield ratio, and PageRank centrality has a positive effect on the environmental loading ratio. This paper updates the Chinese sectoral emergy assessment, identifies key sectors in the Chinese ecological network, and analyzes the impact of network indicators on sectoral emergy performance. These analyses will be useful in updating the guide for adjusting industrial structure in China. [ABSTRACT FROM AUTHOR]

The commercial sector is one of the key sectors in China’s economic transformation and carbon emissions control. This paper applies VAR and STIRPAT models to investigate the major influencing factors of CO 2 emissions in China’s commercial sector over the period 1980–2014. According to the empirical results, per capita GDP, urbanization and energy structure have positive effects on CO 2 emissions while energy intensity has negative effect on CO 2 emissions in the long term. Besides, per capita GDP ranks as the most important driving factors of CO 2 in China’s commercial sector, followed by urbanization, energy structure and energy intensity in that order. This paper argues that reducing energy intensity and improving energy structure are the means to realize CO 2 emission reductions in the commercial sector in the short and long term, respectively. Besides, this paper forecasts that CO 2 emissions in China’s commercial sector will be 1942 Mt by 2030, which is huge and about half of the CO 2 emissions of the European Union in 2014. The findings have important implications for policy-makers to enact CO 2 emission reduction policies. [ABSTRACT FROM AUTHOR]

In this letter, we respond to the articles “Decoupling global environmental pressure and economic growth: scenarios for energy use, materials use and carbon emissions” by Heinz Schandl et al. (Journal of Cleaner Production, 132, 45–56) and “Australia is ‘free to choose’ economic growth and falling environmental pressures” by Hatfield-Dodds et al. (Nature, 527, 49–53). The authors of these papers suggest that policy settings are crucial for Australia to allow growing standard of living to coincide with lower environmental pressure and impact. Both papers make it clear that decoupling has not occurred in past, but stress that this does not mean it cannot occur in the future. Here, we undertake a comprehensive trend analysis by putting their decoupling scenarios into a historical context to assess the feasibility of decoupling economic growth from environmental impacts. To this end, we undertake a structural decomposition analysis to examine the drivers of a change in Australia's greenhouse gas emissions from 1976 to 2050. Using the well-known I = PAT equation, we appraise the contribution of population growth, affluence and technology in accelerating or decelerating the growth of emissions over the 75 year period. Our results suggest that population growth and affluence are key accelerators of greenhouse gas emissions. We argue that in order to decrease emissions from fuel consumption, unprecedented improvements in emissions intensity will be required both in Australia and globally. [ABSTRACT FROM AUTHOR]

Abstract As a significant basic industry for the national economic development, coal chemical industry is one of the main contributors to energy consumption and CO 2 emission. In this paper, the CO 2 emission in the time series of 2020 and 2030 was calculated and the Logarithmic Mean Divisia Index (LMDI) method was employed to explore the driving forces of CO 2 emission change. According to the industry development, the CO 2 emission in 2020 and 2030 was tentatively predicted. The results show that China's coal chemical industry has experienced a significant increase in CO 2 emission, from 177.87 million ton CO 2 (MtCO 2) to 427.19 MtCO 2 in 2005–2015, with an average annual growth rate of 9.16%. Currently, the coal-to-ammonia industry is the largest contributor. According to the decomposition analysis, the economic growth and energy intensity are the two positive factors and the industry structure is a negative factor to carbon emission change. In 2020, the CO 2 emission will be 617.34 MtCO 2 under slow development scenario and 759.69 MtCO 2 under rapid development scenario. In 2030, the CO 2 emission will reach 1041.90 MtCO 2 and 1440.13 MtCO 2 under slow and fast development scenarios, respectively. The coal-to-olefins industry will take the place of coal-to-ammonia and turn to be the largest one in 2020 and 2030 if no CO 2 capture, utilization and storage (CCUS) technologies are adopted. It is probable that the contribution from coal-to-methanol will also play important role for CO 2 emission due to its rapid development. In addition, by 2030, if coal-to-natural gas industry develops as planned, its CO 2 emission will significantly increase, which has the potential to be equivalent to that of olefins industry. Highlights • The CO 2 emission evolution trend in China's coal chemical industry is analyzed. • The major driving factors of CO 2 emission change are identified and the effect sizes of various factors are evaluated. • The CO 2 emission in 2020 and 2030 is forecasted. • Policy implications to policymakers are provided. [ABSTRACT FROM AUTHOR]

Abstract Economic development has promoted urbanization in the Beijing-Tianjin-Hebei (BTH) region. With the improvement of living standards, residential consumption have become an important contributor to regional carbon emissions. Using an input-output model, we calculated direct and indirect carbon emissions from both urban and rural residential consumption in the BTH region from 2002 to 2012, and then explored the main factors contributing to indirect carbon emissions from residential consumption using a structural decomposition analysis. Results showed that direct carbon emissions from residential consumption increased from 47.13 million tons (MtC) in 2002 to 123.34 MtC in 2012 and indirect carbon emissions from residential consumption increased from 210.36 MtC in 2002 to 550.00 MtC in 2012. Carbon emission intensity and residential consumption level were the main factors affecting indirect carbon emissions during 2002–2012, with varying effects in different sub-regions. Carbon emission intensity played negative effects in Beijing (−37.19 MtC) and Tianjin (−55.39 MtC), positive effects in Hebei (92.89 MtC). Residential consumption level played positive effects in the three sub-regions (79.94 MtC in Beijing, 31.91 MtC in Tianjin and 155.52 MtC in Hebei). Increases in intermediate demand, residential consumption level and population size enhanced indirect carbon emissions. Based on the analysis, this paper contributed to carbon emission reduction strategies in the BTH region. Highlights • Direct carbon emissions from residential consumption increased to 123.34 MtC in 2012. • Indirect carbon emissions from residential consumption increased to 550.00 MtC in 2012. • Carbon emission intensity and residential consumption level were the main impact factors. • Intermediate demand, residential consumption level and population size played positive effects. [ABSTRACT FROM AUTHOR]

The Environmental Kuznets Curve Hypothesis (EKC) which argues that an inverted U-shaped relationship exists between economic growth and environmental degradation has been examined by numerous studies for different countries or country groups. However, the validity of the EKC hypothesis at the sectoral level is mostly ignored. In addition, most of these studies have modeled the nexus between per capita income and CO 2 emissions based on the assumption of non-linearity. Unlike previous studies, the main purpose of this paper is to investigate the validity of EKC hypothesis for sub-elements of carbon dioxide emissions (i.e. total CO 2 emission, commercial CO 2 emission, electrical CO 2 emission, industrial CO 2 emission, residential CO 2 emission and transportation CO 2 emission) in the United States for the annual data of 1973–2015. In doing so, the rolling window estimation procedure is employed to observe the effect of per capita income on sectoral CO 2 emission for each sub-sample period instead of the non-linear assumption. The results of the rolling window coefficients show that inverted U-shaped EKC hypothesis is valid for total CO 2 emission, industrial CO 2 emission, electrical CO 2 emission and residential CO 2 emission. However, the inverted U-shaped relationship between economic growth and CO 2 emission is not supported for commercial and transport sector of the US. [ABSTRACT FROM AUTHOR]

High-Carbon Consumption Behavior (HCCB) of urban residents is one of the main obstacles to the goals of low carbon development in China with the rapid development of the national economy. Among all the uptrend influences, the one caused by conspicuous consumption is worth more attention. In this paper, a structural equation model was applied to analyze the impact path of conspicuous consumption motivation on HCCB. Multiple group analysis was then used to test the moderating effect of individual face conscious and demographics on the impact. Our data originated from a social study of Xuzhou, Jiangsu province, China (n = 351). The results indicated four leading aspects. First, the raising habitual HCCB is mainly affected by three points, materialistic hedonism, interpersonal mediation and social status demonstration, and the influence is reduced in successively. While communication of belonging to/dissociation from a group and ostentation have little effect on habitual HCCB. The second, purchased HCCB of residents is mainly impacted positively by materialistic hedonism, and other factors have weak influence on it. The third, face consciousness has a significant moderating effect on the relationship between residents’ conspicuous consumption motivation and HCCB. People with strong face consciousness are more affected by the social status demonstration and interpersonal mediation; those with weak face consciousness are only driven by material hedonism. The last, gender, marital status, age, education level, and household monthly disposable income have significant moderating effects on the relationship. Among them, the HCCB from males, young people, married persons, highly educated people, and higher income groups is more affected by materialistic hedonism, interpersonal mediation, and social status demonstration, while from females with lower education, and lower income groups is more likely to be driven by social status demonstration. [ABSTRACT FROM AUTHOR]

This study reinvestigates empirically the Environmental Kuznets Curve (EKC) hypothesis. According to this hypothesis, there is an inverted U-shaped relationship between environmental degradation and economic growth. The available EKC literature, in general, employs CO 2 emissions as a proxy for environmental degradation. Unlike the current studies, this study employs the Ecological Footprint (EF hereafter) variable, instead of CO 2 emissions, as an indicator of environmental deterioration. Then, this research observes the countries which are classified into three income groups in order to investigate EKC hypothesis. In conclusion, following the second generation panel data techniques, this paper reaches its best fitted model among the models of continuously updated fully modified (CUP-FM) and continuously updated bias corrected (CUP-BC) models, and, reveals that EKC hypothesis is confirmed by low income, middle income and high income group countries’ data. [ABSTRACT FROM AUTHOR]

Cities have been one of the most important areas of CO 2 emissions. It is increasingly important to research the effect of urbanization on CO 2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO 2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000–2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO 2 emissions efficiency (the ratio of the target CO 2 emissions to the actual CO 2 emissions). The results show that the average CO 2 emissions efficiency was 0.959 in 2010, and CO 2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO 2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO 2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO 2 emissions efficiency, and there is a U-curve relation between CO 2 emissions efficiency and urbanization, indicating that CO 2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO 2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO 2 emissions efficiency. This research highlights the effect of urbanization on CO 2 emissions efficiency and the importance of improving CO 2 emissions efficiency in developing countries. [ABSTRACT FROM AUTHOR]

Carbon Emissions Pinch Analysis is a variant of Pinch Analysis that was developed for macroscale carbon-constrained energy planning problems. It uses carbon intensity as the measure of quality of energy streams to be allocated within a large-scale system. Since its inception, this method has been applied to various systems at different scales, utilized in different geographic contexts, and also extended to allow different sustainability metrics, such as land and water footprints, to be considered. In this paper, Carbon Emissions Pinch Analysis is further extended by combining it with Input-Output Analysis. This hybrid approach is capable of economy-wide analysis, where the segments of composite curves are comprised of the sectors in an economic system; flow quantities are measures in terms of economic value, while quality is indicated by carbon intensity. After first illustrating this approach to a small pedagogical case study, the methodology is then applied to a case study on the carbon-constrained economic growth of the Philippines. Prospects and limitations of the potential application of this methodology to other cases are also discussed. [ABSTRACT FROM AUTHOR]

The surge in energy consumption and CO 2 (carbon dioxide) emissions is exacerbating global warming. The Chinese government has promised to reduce CO 2 emission by 60%–65% per unit of GDP over the 2005 level by around 2030. Analyzing the factors influencing CO 2 emissions is one of the key aspects involved in effectively reducing greenhouse gas emissions. In this paper, we add climatic factors based on socio-economic factors, and adopt the extended STIRPAT model to investigate the mechanism driving emissions by the co-action of natural and socio-economic factors. The results show that the 1995–2014 emissions in China's Zhejiang province had an upward trend with growth rates varying between intermediate periods. The order of the factors' elasticity coefficients is urbanization rate (0.181) > GDP per capita (0.180) > population (0.177) > energy intensity (0.170) > trade openness (0.170) > cooling degree days (0.127) > mean temperature anomaly (0.116). Economic development and urbanization were the main factors promoting CO 2 emissions in 1996–1999, while economic development and foreign trade were the main factors in 2000–2014. Policy makers should pay more attention to keeping a healthy pace of economic development, introducing more low energy consumption and low pollution enterprises and encouraging technological innovation. Improving the quality of urbanization, guiding a sustainable lifestyle, adjusting the structure of foreign trade and curbing energy intensity are also expected to reduce emissions. [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]

Currently, China is the largest emitter of carbon dioxide in the world. Moreover, the manufacturing industry is the largest contributor to China's CO 2 emissions. Therefore, investigating the main driving forces of CO 2 emissions in the manufacturing industry has great practical significance. Using panel data from 2000 to 2014, this paper examines the driving forces of the industry's CO 2 emissions at the regional level. The results show that economic growth dictates the industry's CO 2 emissions, and its impact varies across regions. The influence of industrialization on CO 2 emission in the central region is stronger than in the eastern and western regions due to differences in industrial structure and the building industry. The impact of urbanization declines continuously from the western region to the central and eastern regions because of differences in human capital accumulation and private car ownership. Energy structure also produces a different effect in the three regions on account of differences in coal consumption. Furthermore, the role of energy efficiency in reducing CO 2 emissions in the three regions is markedly different owing to big differences in R&D funding and R&D personnel investments. Considering the significant regional differences, we propose policy recommendations to mitigate CO 2 emissions in the manufacturing industry. [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]

Given the background of global warming, carbon emission reduction has become a topic of global importance. Land use change not only influences carbon storage in terrestrial ecosystems directly, but it also indirectly affects anthropogenic carbon emissions, which occur more frequently in coastal regions. Based on data of energy consumption, industrial products, waste, soil organic carbon, and vegetation, together with land use images of five typical years, this paper calculated the total carbon emissions in coastal Jiangsu, China, assigned the detailed carbon emission items to different land use types, and optimized land use structure to low carbon emissions using the Linear Programming Model. It was found that carbon emission intensity in coastal Jiangsu was much higher than the average for China as a whole, and that energy consumption contributed most to local carbon emissions with the contribution from animals second. Urban land accounted for the most concentrated and highest intensity of carbon emissions. Between 1985 and 2010, the transfer of cropland to built-up land accounted for the largest percentage of the total transferred area and contributed most to the increase of carbon emissions. In particular, the limitation of urban land will play a key role in carbon emission reduction. Our optimized land use structure can control and decrease carbon emissions effectively and thus, it is an important tool worth the consideration of land managers and policy makers. [ABSTRACT FROM AUTHOR]