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In 2017, a carbon emissions trading market will be launched nationwide by the China government. Calculating the enterprise carbon emissions is an important prerequisite and basis for trading. This paper discussed types of greenhouse gases, calculation boundaries and methods, energy consumption, carbon emissions, and intensity of a representative integrated pulp and paper mill in China based on China Guidance and Greenhouse Gas Protocol Tools. The results showed that there were 435,000 tonnes (t) of CO2 emissions from that mill in 2014 that did not contain emissions of biomass energy, which was 8 times higher than that of fossil energy. The pulp carbon intensity based on the mill was 0.230 t CO2/Adt, which accounted for 7.50% to 57.4% in other pulps' based on the product. Intensity based on Gross Domestic Product (GDP) was 1.090 t CO2e/1000 USD and accounted for 56.8% intensity in the China paper industry. The intensity based on sales accounted for 52.6% in the firstclass enterprise in the developed country. It also showed that intensity was influenced by the species of raw material, energy, and products, which provided the mill with measures for energy saving and emissions reduction to obtain the redundant carbon emissions in the trading. [ABSTRACT FROM AUTHOR]

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO2 emissions dataset (PKU-CO2) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient. [ABSTRACT FROM AUTHOR]

The issue of China's CO2 emissions has become a major focus of the growing concerns, globally, for its potential impact on Climate Change. In this paper, supply chain paths that drive changes in life cycle CO2 emissions in China from 1992 to 2007 were identified, based on SPD (structural path decomposition) analysis of China's input–output tables and energy demand changes, over this study period. The study period was divided into three stages: 1992–1997, 1997–2002 and 2002–2007. The influence on changes of CO2 emissions derived from final demand was divided into six factors: direct CO2 emission intensity (e); industrial structure (A); product structure of final demand (ψ); category composition of final demand (Δ); per capita final demand (Y), and population (P). The detailed analysis reveals that direct CO2 emissions intensity (e) was the dominant factor in reducing China's CO2 emissions, while per capita final demand (Y) was the major single factor increasing the emissions. The largest influence on CO2 emissions from the viewpoint of supply chain paths was found to be “Chemical industry → exports”. The supply chain paths with high emissions at different stages during the study were “Metal smelting → exports”, “Nonmetallic products → fixed capital” and “Metal smelting → fixed capital”, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This paper uses nonparametric efficiency analysis technique to estimate the energy efficiency, potential emission reductions and marginal abatement costs of energy-related CO2 emissions in China. We employ a non-radial slacks-based data envelopment analysis (DEA) model for estimating the potential reductions and efficiency of CO2 emissions for China. The dual model of the slacks-based DEA model is then used to estimate the marginal abatement costs of CO2 emissions. An empirical study based on China’s panel data (2001–2010) is carried out and some policy implications are also discussed. [Copyright &y& Elsevier]

Abstract: Reducing environmental pressure is a major concern for China but despite the improvements in energy efficiency, its gross carbon dioxide (CO2) emissions have maintained an upward trend, consistent with the Jevons Paradox. A fundamental shift is thus needed at all levels, including the household. This paper explores the embedded carbon footprint (ECF) of Chinese urban households associated with activities, such as food, personal transport, communications, education, recreation, health and hygiene. It uses an input–output model for carbon emissions and 2003–2009 urban household data. The results show that the total ECF emission intensity increases when an individual''s consumption is higher than 10,000 Yuan (¥). Structural changes are further observed with consumption expenditure above ¥10,000: food, clothing and other survival-oriented emissions intensities as well as education, health and development-oriented emissions intensities reduce; transport, recreation, housing and enjoyment-oriented emissions intensities increase or remain stable and the total emissions intensity increases. Currently per capita consumption expenditure of Chinese urban residents exceeds ¥10,000 and as income continues to rise, China will remain on a high-carbon track. There is a need for better policies, management and behavioural change and the study provides some policy suggestions, including a carbon quota system to guide individual consumption. [Copyright &y& Elsevier]

Abstract: Civil aviation in China has developed rapidly in recent years, and the effects of civil aviation emissions on the atmospheric environment should not be neglected. The establishment of emission inventories of atmospheric pollutants from civil aviation contributes to related policy formation and pollution control. According to the 2010’s China flight schedules, aircraft/engine combination information and revised emission indices from the International Civil Aviation Organization emission data bank based on meteorological data, the fuel consumption and HC, CO, NO x , CO2, SO2 emissions from domestic flights of civil aviation in China (excluding Taiwan Province) in 2010 are estimated in this paper. The results show that fuel consumption in 2010 on domestic flights in China is 12.12 million tons (metric tons), HC, CO, NO x , CO2 and SO2 emissions are 4600 tons, 39,700 tons, 154,100 tons, 38.21 million tons and 9700 tons, respectively. The fuel consumption and pollutant emissions of China Southern Airline are responsible for the largest national proportion of each, accounting for 27% and 25–28%, respectively. [Copyright &y& Elsevier]

Abstract: The interplays among economy and energy and environment have been widely concerned. This paper put forward several indicators to quantify the relationships among economic growth and energy consumption and CO2 emission. As an example, these indicators were applied to evaluate the comprehensive performances of China during 1990–2007. The results show that Chinese people has been living a better life with Chinese rapid economic growth but not synchronously in urban and rural areas. Non-carbon energy resources share has increased; however, fossil energy resources have still acted as the main driver for Chinese economic growth during this period. Technical progress has improved the fossil energy efficiency of Chinese economic activity, which leads to CO2 emission per unit GDP and CO2 emission per capita unit GDP dropping simultaneously; however, the two indicators’ annual decline rates become smaller and smaller, which reflects that technical progress’ role is dropping and economic scale''s effect is climbing. People''s survival has a rising contribution to CO2 emission. CO2 emission per capita has increased, which shows that economic scale has greater impact on CO2 emission than technical progress does. Relatively speaking, Chinese development patterns have become more and more sustainable during this period. Finally, based on the related issues being discussed, some corresponding suggestions are put forward for Chinese government to further coordinate the relationship among economic development and energy consumption and CO2 emission. The proposed indicators can form a set of useful tool for policy-makers to promote the harmonious development of economy and energy and environment in different regions and countries. [Copyright &y& Elsevier]

Abstract: This paper examines the driving forces for reducing China’s CO2 emission intensity between 1998 and 2008, utilizing the logarithmic mean divisia index (LMDI) technique. By first grouping the CO2 emissions into two categories, those arising from activities related to the electric power industry and those from other sources, emission intensity is further broken down into the effects of the CO2 emission coefficient, energy intensity of power generation, power generation and consumption ratio, electricity intensity of the gross domestic product (GDP), provincial structural change, and the energy intensity of the GDP for other activities. The decomposition results show that improvements in the energy intensity of power generation, electricity intensity of GDP, and energy intensity of GDP for other activities were mainly responsible for the success in reducing China’s CO2 emission intensity and that activities related to the electric power industry played a key role. It is also revealed that performance varied significantly at the individual province level. The provinces with higher emission levels contributed the most to China’s improvements in CO2 emission intensity. [Copyright &y& Elsevier]

Abstract: This paper uses multivariate co-integration Granger causality tests to investigate the correlations between carbon dioxide emissions, energy consumption and economic growth in China. Some researchers have argued that the adoption of a reduction in carbon dioxide emissions and energy consumption as a long term policy goal will result in a closed-form relationship, to the detriment of the economy. Therefore, a perspective that can make allowances for the fact that the exclusive pursuit of economic growth will increase energy consumption and CO2 emissions is required; to the extent that such growth will have adverse effects with regard to global climate change. [ABSTRACT FROM AUTHOR]