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Sustained increase in Chinese household income has exerted positive impact on the consumption of goods and services. This paper aims to analyze the unequal impacts of household consumption across different income households on the whole CO 2 emissions, by utilizing a semi-closed input-output model and hypothetical extraction method. The semi-closed input-output model moves household consumption activities into the intermediate use along with dividing urban and rural households to several groups by their income levels, and the hypothetical extraction method (HEM) is used to estimate the reductions of CO 2 emissions relative to total emissions after extracting the consumption of each household group. The results show that: (1) the impact on CO 2 emissions generated by urban households’ consumption are 1.8 times as much as that of rural ones; (2) excluding consumption of higher-income households from the economy would cause greater CO 2 emission reductions than lower ones. Specifically, urban highest-income households have the most relative reductions of CO 2 emissions, which are −12.2% in 2007 and -13.8% in 2012, while for rural highest-income households are −5.1% in 2007 and -5.1% in 2012. And urban and rural highest-income households are respectively 3.8 and 1.8 times on the relative reductions of the lowest-income ones; (3) the relative reductions of household CO 2 emissions from food sectors is the most, while from residence sectors is the least. Based on the above conclusions, this paper puts forward consumption-based policy implications. [ABSTRACT FROM AUTHOR]

China has become the global largest CO 2 emitter, and CO 2 emissions from China’s power industry account for almost half of the total. Therefore, the power sector plays a key role in achieving low-carbon economy. It is imperative to formulate comprehensive policy implications for the power sector from both macro and micro perspectives to achieve the target of emission reduction. This paper selected seven socio-economic and technological factors involved in the whole power industry to investigate the mechanism driving CO 2 emissions based on the extended STIRPAT model. The results show that the order of factors’ absolute elasticity coefficients is GDP per capita (0.245) > power generation efficiency (−0.237) > power consuming efficiency (−0.189) > line loss rate (−0.184) > electricity practitioner population (0.143) > industrial structure (0.082) > generation structure (0.068). Further, it is necessary to discuss how a specific power enterprise responds under the low-carbon generation policy to maximize its own interest. The evolutionary game model between power enterprise and government is established and results indicate that it is critical to slash low-carbon production cost, enhance returns on low-carbon production and strengthen the regulation of power enterprises, and reduce the supervision cost of government. Accordingly, policy recommendations are proposed from both macro and micro perspectives for CO 2 emission reduction of power industry. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2 ) emissions have become a rising concern in China. Few studies have considered spatial correlation and agglomeration effect of CO 2 emissions for adjacent regions and provinces. This paper employs Geographical Weighted Regression (GWR) model to examine the impact of urbanization, industrial structure and energy intensity on CO 2 emissions and reveals the spatial correlation in different provinces in 2005, 2008, 2011 and 2015. The results indicate that there is an obvious spatial effect on CO 2 emissions of each province based on the GWR results. Urbanization is the most significant factor in the increase of CO 2 emissions for all provinces in each year. For the neighboring provinces, a coordinated low-carbon urban construction plan should be carried out based on the urbanization development level. Energy intensity has a positive effect on CO 2 emissions, but the effect on the emission reduction is relatively weak and unstable. It should strengthen exchanges and cooperation between provinces and regions by jointly exploring and promoting technologies to improve the efficiency of resource use and reduce CO 2 emissions. The influence of industrial structure on CO 2 emissions is positive, indicating that the industrial structure adjustment plays an important role in carbon emission reduction. [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]

In assuming its commitments to the reduction of CO 2 emissions, China is contributing great efforts to mitigate CO 2 emissions across its all economic sectors including construction industry. The Chinese construction industry is one of the main contributors to CO 2 emissions in China. This paper presents an empirical study on the CO 2 emissions in the Chinese construction industry across the period of 1995–2011. The input-output analysis method is employed for conducting the study. The data used for analysis are mainly collected from the world input-output database. The analysis results demonstrate that the CO 2 emissions in the Chinese construction industry increased by 388.7% from 1995 to 2011, reached to 3035.9 Mt in 2011. However, the carbon intensity in the construction industry decreased by 46.9% from 1995 to 2011, recorded as 0.00198 Mt/million dollars in 2011. The indirect CO 2 emissions were the majority of the construction CO 2 emissions, accounting for over 95% during 1995–2011. The process-based CO 2 emissions contributed 8.7%–12.6% of the total construction CO 2 emissions during 1995–2011. The electricity, gas and water supply industry, the other non-metallic mineral industry, and the basic metals and fabricated metal industry were the main contributors to the construction CO 2 emissions during 1995–2009. This study presents a comprehensive picture on the CO 2 emissions in the Chinese construction industry, which provides valuable references both to further study and to formulate more effective policies. [ABSTRACT FROM AUTHOR]

Cities contribute to most of the CO 2 emissions. And the economic system at city level is much complex due to various linkaged sectors. This paper aims to analyze the economy-wide contribution of sectors and households to CO 2 emissions in Beijing (China) by utilizing a semi-closed input-output model integrated with a modified hypothetical extraction method. Results show that, compared with 2005, in 2012 (1) interprovincial export caused the largest amount of CO 2 emissions [135.5 Mt] with the main contributions arising from manufacturing (42.1 Mt); transportation, storage, and post (TSP in short, 29.1 Mt); and households (23.6 Mt); (2) across the intermediate input-output system, real estate activities accounted for the largest amount of embodied CO 2 intensity (0.07 kg per yuan) and more sectors outsourced CO 2 ; (3) tracing the integrated sector network, CO 2 linkages pointed to manufacturing and TSP dominating the internal linkages, manufacturing prominent in mixed linkages, secondary industry leading the net forward linkages, and tertiary industry dominant in terms of net backward linkages, helping control CO 2 according to its origin; (4) CO 2 emissions induced by household strikingly affected total CO 2 emissions in Beijing, mainly coming from income-oriented affects, with a large rural-urban disparity and a similar sectoral distribution pattern. Finally, we propose suggestions on carbon reduction in terms of technological interlinkages, final demand and household participation. [ABSTRACT FROM AUTHOR]

Forecasting CO 2 emissions in China always has been of great significance as it could help the government to improve energy policies and plans. To this end, a novel grey multivariable model is designed in this paper. Compared with the conventional grey multivariable model, which has certain drawbacks of inaccurate prediction and poor adaptability that restrict their applications in practical cases, the proposed model can make three improvements: first, an optimized grey model having a modified background value is proposed to predict the trends of the driving variables. Second, the novel grey multivariable model is established, combined with the changing trends of driving variables. Third, the adjustment coefficient in the new model is optimized to obtain optimal values for the time response function. To demonstrate its efficacy, the proposed model is employed to reproduce and predict the CO 2 emissions from fuel combustion compared with four benchmark models—the results show that the new model yields more accurate forecasting results than the competing models. Eventually, the new model will be used to quantify future Chinese CO 2 emissions from fuel combustion from 2014 to 2020, and the forecasted results can provide a solid basis for formulating environmental policies and energy consumption plans. [ABSTRACT FROM AUTHOR]

To guide the adjustment of the electricity policies of the 13th Five-year Plan and the present market-oriented reforms, this paper performs a scenario analysis of the CO 2 emissions from China's electric power industry. By using a logarithmic linear equation to explain the relationship between CO 2 emissions and their influence factors, a hybrid model to forecast the “natural” development of the explanatory variables, and emission-relative data from 2001 to 2013 to estimate the equation parameters, five scenarios are designed, and the CO 2 emissions of each scenario are forecasted for the period of 2016–2030. On the basis of the modeling results, the sensitivity and contribution of each variable are also measured. Empirical analysis draws the following conclusions. (1) The electric power industry of China cannot easily reach its CO 2 emission peak before 2030. This will impose considerable pressure on the Chinese government to realize its emission mitigation target. (2) Compared with the non-fossil energy share in electricity generation, the CO 2 emissions from China's electric power industry are more sensitive to the changes in total electricity consumption and thermal power generation efficiency. (3) The increase in total electricity consumption is the single most important contributor to CO 2 emission growth from the electric power industry of China. (4) To mitigate future CO 2 emissions from the electric power industry, the Chinese government should optimize the industrial export structure and enhance its awareness of the increase in household electricity consumption. [ABSTRACT FROM AUTHOR]

This paper presents a subsystem input–output model to analyse the patterns behind the evolution of CO 2 emissions in energy-intensive industries. Two emissions accounting principles (the production accounting principle and the consumption accounting principle) are applied. The embodied CO 2 emissions are divided into the production-based CO 2 emissions associated with three components (internal component (IC), external component (EC) and demand level component (DLC)) in addition to the CO 2 emissions identified by the indirect component (INC) from a consumption perspective. A structural decomposition analysis is further conducted to decompose the emissions changes into 3 effects, which are the emissions intensity effect (ET), the technological effect (TT) and the demand effect (DT). The results show that the external component is primarily responsible for increased CO 2 emissions and the demand effect is the key factor in the decrease of CO 2 emissions. Moreover, sectors with a positive difference between production-based and consumption-based emissions are more sensitive than the others to the technological changes. Thus, different measures should be adopted by considering sectorial realities. [ABSTRACT FROM AUTHOR]

In a joint U.S.–China statement on climate change , the Chinese government declared to peak CO 2 emissions around 2030. This demonstrates the government's determination to deal with carbon emission and climate change. Although the food industry is not a carbon emission-intensive industry, its large scale makes the emission reduction in the industry very important. Based on the input–output structural decomposition method, this paper calculates the CO 2 emissions of China's food industry from 1991 to 2012, and decomposes the change in CO 2 emissions of the industry during 1992–1997, 1997–2002, 2002–2007 and 2007–2010 into four main effects: emission factor, energy structure, energy intensity, and total output (including four sub-effects: intermediate use, domestic final demand, import substitution and export extension). The results show that changes in CO 2 emissions in the food industry mainly depends on total output effect and energy intensity effect. Energy intensity effect is the most important factor reducing CO 2 emissions, as it reduced cumulative 213 million tons (Mt) CO 2 emissions of the industry. Among total output effect, the effects of intermediate use and domestic final demand are the two biggest contributors to carbon emissions. Finally, we provide some policy advices for constraining and reducing CO 2 emissions of China's food industry. The advices include increasing R&D investment and the substitution of energy with other input factors to decrease energy intensity, increasing the added value of the food industry, optimizing the energy structure with more clean and low-carbon energy, and maintaining the prices of raw materials of the food industry (i.e. agricultural products) with taxes or subsidies. [ABSTRACT FROM AUTHOR]