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Emerging economies like China, India, and Mexico are becoming the world's manufacturing leaders through the engagement in offshoring production activities. In achieving this status, however, they have met with difficult environmental issues. This paper features an analysis of industry-level panel data to examine the link between different offshoring tasks in China and carbon dioxide emissions. Through this analysis, the environmental effects of offshored production are addressed. Specifically an input–output model is presented, thereby quantifying offshored production tasks in China. The results show that iron and steel, nonferrous metals (IS) and chemicals (CH) generate the most carbon dioxide during production of processed goods. Additionally, electrical machinery (EM) and general machinery (GM) generate a higher amount of carbon dioxide during production of parts and components. The results also show that those industries characterized by higher energy consumption have higher carbon dioxide (CO 2 ) emissions induced by offshoring. Given these results, it seems that any solutions geared towards reducing the energy consumption of entire manufacturing industries would be useful. Furthermore, the results of the analyses presented here indicate that China would be responsible for working with developed countries to reduce carbon dioxide emissions through cooperative environmental regulation. A globalization perspective is adopted to offer unique insight into the environmental issues faced by China. The results of this paper would provide quantified, actionable information for formulating practical industrial policies geared towards reducing carbon dioxide leakage. [ABSTRACT FROM AUTHOR]

Abstract Theses on China's low-carbon economy focus on the driving forces of carbon emissions, energy–carbon efficiency, and the carbon–economy nexus. Meanwhile, comprehensive frameworks that assess China's green economy and also analyze its driving forces are lacking. This paper constructs a green productivity index based on the Global Malmquist–Luenberger productivity index to evaluate the development of the low-carbon economy in China. A panel vector auto-regression model is adopted to further analyze the endogenous interactions and dynamic relationships among aspects of the green economy and its influencing factors. Empirical results show that China has seen a balanced development of green economic growth in 2000–2016, achieved in a series of clear stages. To maintain the sustainable growth of the low-carbon economy in the future, China must rely more on technological progress. This research also makes some important discoveries that have not been found in previous studies: the impact of China's industrial structure and energy consumption structure on the green economy are short term, having a duration of impact of approximately four years. Finally, we provide an assessment of provincial green economy development and describe the corresponding policy implications. Graphical abstract Image Highlights • A comprehensive analysis of China's green economy. • A slack-based Global Malmquist–Luenberger index is adopted to calculate the Green Productivity Index in China. • The stage characteristics of green economic development are identified. • A panel vector autoregressive model is used to analyze the interactions among aspects of the environmental economic system. • The findings that the impact duration of energy structure, industry structure, and urbanization on the green economy is about four years. [ABSTRACT FROM AUTHOR]

Reliable energy statistics serve as the foundation and starting point for understanding the climate policies of any country. As the most comprehensive sources of energy data for China, China Energy Statistics Yearbooks (ESY), published by the National Bureau of Statistics (NBS), underwent three major revisions since 2005. This paper analyzes how these three major revisions correspond to data on China's energy use from 1996 to 2012, and theirs impacts on CO 2 emissions, emission intensity and the share of non-fossil energy in total energy consumption, which are key indicators of China's climate policies. Some notable changes include CO 2 emissions for 2012, which increased by 594 Million tons – a figure equivalent to South Korea's annual emissions, which implies that China's emissions at peak level will be higher than previous estimation. Meanwhile, revised emission intensity data show a higher amount and lower decreasing rate, which means that China's achievements in decreasing emission intensity during 2005–2012 have been overestimated, while future difficulties for achieving the goal of 40–45% reduction will be greater. On the share of non-fossil fuel energy in China's total energy mix, the adjustments increased the indicator by 1.26%, reflecting the fact that China's effort in developing non-fossil energy had been underestimated. Discrepancies in reported scattered coal in different statistics versions are the main contributors to these changes. To further enhance the reliability of its energy statistics, and thereby its climate policies, China should remove incentives for misreporting through legal means, and make the technical system for data collection more comprehensive and accurate, with particular emphasis on scattered coal. [ABSTRACT FROM AUTHOR]

As one of the largest energy consumers, the transport sector generated direct and indirect emissions which significantly affect the environment, accounting for approximately one-third of the total carbon emissions. While the major impacts are from transport energy use and emissions, very few studies attempted to examine the impacts from transport infrastructure, especially at a city or country level. This paper, taking Shenzhen in China (a fast developing megacity) as the case study, is specially designed to quantify the carbon footprint of the urban roads by using streamlined life cycle assessment method. For given years (ranged from 2004 to 2013), various activities of urban roads (e.g. newly planned road construction, maintenance of road in use, and road renovation and demolition) have been examined in this study. The results show that the total carbon footprint from urban roads in Shenzhen was 260 (±20) thousand tons CO 2 e in 2013. The major contributor was the materials use (embodied impact) from newly constructed roads, which accounts for 52.3% of the total carbon footprint, followed by the maintenance stage (24.3%). The eco-design process of road construction plays a vital role in achieving the effective carbon footprint reduction. These findings help to better understand the carbon footprint from urban roads in megacities, and provide useful inputs for policy making process in terms of identifying carbon reduction opportunities for the transport sector. In addition, the methodologies are useful for the quantification of carbon footprint in other cities of China and beyond. [ABSTRACT FROM AUTHOR]

Accurately understanding the real situation of China’s carbon emissions at the provincial level is not only crucial for realizing carbon mitigation in China, but also of great importance for tackling climate change on a global scale. This paper uses detailed energy consumption data subdivided within each fuel type and sector-specific emission factors, to calculate both direct and indirect energy-related CO 2 emissions for China’s provinces, and offers provincially-targeted policy proposals on emission reductions. The results uncover large divergences in calculation of provincial CO 2 emissions between production-based and consumption-based accounting approaches. Considering the large amount of interprovincial CO 2 emission transfers embodied in secondary energy deployment, the consumption-based accounting performs better in reflecting the actual features and responsibilities of carbon emissions in the provinces of China. From the consumption perspective, Shandong, Hebei, Jiangsu, Inner Mongolia, Guangdong, Henan, Liaoning and Shanxi contributed to the majority of China’s carbon emission growth during the period of 1996–2013, while Xinjiang, Sichuan, Anhui, Tianjin and Hunan were playing an increasingly important role. These two groups of provinces should be regarded as the key regions for national emissions abatement. Meanwhile, the energy industry was the top sectoral contributor to CO 2 emissions growth in most provinces, while service and household sectors made increasingly significant impacts on driving up provincial emissions. Together with ongoing efforts in the energy sector, the mitigation of CO 2 emissions in service and household sectors deserves much more attention, especially in the transportation and urban household sectors. Furthermore, the temporal trends of carbon emissions in various provinces, as well as their factor and sectoral driving forces, varied greatly with each other. Therefore, each of the provinces should formulate emission reduction policies according to their own situation, and target the key driving factors and sectors of carbon emissions at the current stage or in the near future. [ABSTRACT FROM AUTHOR]

The use of data envelopment analysis (DEA) in China’s regional energy efficiency and carbon emission efficiency (EE&CE) assessment has received increasing attention in recent years. This paper conducted a comprehensive survey of empirical studies published in 2006–2015 on China’s regional EE&CE assessment using DEA-type models. The main features used in previous studies were identified, and then the methodological framework for deriving the EE&CE indicators as well as six widely used DEA models were introduced. These DEA models were compared and applied to measure China’s regional EE&CE in 30 provinces/regions between 1995 and 2012. The empirical study indicates that China’s regional EE&CE remained stable in the 9th Five Year Plan (1996–2000), then decreased in the 10th Five Year Plan (2000–2005), and increased a bit in the 11th Five Year Plan (2006–2010). The east region of China had the highest EE&CE while the central area had the lowest. By way of conclusion, some useful points relating to model selection are summarized from both methodological and empirical aspects. [ABSTRACT FROM AUTHOR]

The 2015 Paris climate change negotiations represents the most ambitious worldwide effort to date to combat climate change. As the largest energy consumer and carbon emitter, China pledges 60–65% cut in carbon intensity below 2005 levels by 2030. To accelerate China's climate efforts, China has also committed to cap its primary energy use at 4.8 billion tons of coal equivalent in 2020. This paper proposes an equitable and efficient scheme to disaggregate this national goal among provinces. A three-step disaggregation procedure is proposed which takes into account the initial allocation, national redistribution and inter-provincial adjustment. First, this study allocates the incremental energy quota, taking 2010 as the base year. Second, part of the energy quota is set aside by the central government for redistribution purposes, while an incremental energy quota index comprising equity and efficiency criteria is used for initial allocation among provinces. Finally, historical efforts and inter-provincial energy flows are considered for inter-provincial adjustment. This disaggregation scheme is flexible and can be easily extended to allocation and management of other resource and environmental targets. The empirical findings can assist decision-makers in identifying which provinces are likely to over-consume the energy quota relative to the proposed allocation and which provinces are likely to perform better in energy conservation. Moreover, the results show that the energy cap set for 2020 is unachievable without further action. Market-based instruments, such as an energy allowance trading market, are strongly recommended to achieve the energy use target in an equitable and cost-effective manner. [ABSTRACT FROM AUTHOR]

In contrast to the ever-increasing focus on China's CO 2 emissions, little attention has been given to its non-CO 2 greenhouse gases (GHGs). Based on the latest released official GHG emission data, this paper presents an inventory and embodiment analysis of the non-CO 2 GHG emissions covering CH 4 , N 2 O, HFCs, PFCs and SF 6 by the Chinese economy in 2005. The total direct non-CO 2 GHG emissions from economic sectors amount to 1368.5 Mt CO 2 -eq, of which CH 4 contributes 848.4 Mt, N 2 O 356.8 Mt and F-GHGs 163.3 Mt. Responsible for 93.2% of the total emissions are the three sectors of Agriculture , Coal Mining and Dressing and Chemicals . Exports, urban consumption and capital formation derive the major fractions of embodied emissions for final demand. Rural and urban consumption are both non-CO 2 GHG emission intensive, having the largest embodied emission intensities among all final demand categories. The impact of international trade on China's non-CO 2 GHG emissions is significant. The total emissions embodied in exports are 487.0 Mt CO 2 -eq, equivalent to 35.6% of the total domestic emissions. Prominently, the re-exported emissions amount to 135.3 Mt CO 2 -eq due to the processing trade. Textile and Garments and Other Fiber Products, Leather, Furs, Down and Related Products are the top two net embodied emission export sectors. While China's non-CO 2 GHG emissions are expected to increase steadily in the future, both the direct emissions from on site production and the embodied emissions induced by final consumption and international trade need to be addressed and targeted to inform effective mitigation policies. [ABSTRACT FROM AUTHOR]

Abstract: Evaluation of the energy and emissions efficiency of Chinese regions has recently attracted increasing interest. A number of previous studies have contributed to the measurement of energy efficiency using various types of data envelopment analysis (DEA) techniques. However, most of these DEA-based energy efficiency analyses were restricted to the radial expansions of outputs or radial contractions of inputs. In this paper, we utilize the multi-directional efficiency analysis (MEA) approach instead of the traditional radial DEA to investigate Chinese regional energy and emissions efficiency. Since MEA selects benchmarks such that the input contractions or output expansions are proportional to the potential improvement identified by considering the improvement potential in each input or output variable separately, not just the efficiency status but also the efficiency patterns of different Chinese regions and areas can be detected. The empirical study results indicate that, in general, the MEA efficiency of China experienced an increasing process over the study period 1997–2010; the east area overall is more MEA efficient than the central area and the west area of China during the study period; the significant higher MEA efficiency of the east area to the central area and the west area are due to both the higher energy specific efficiency and the higher emissions specific efficiency of the east area compared to the other two areas; the provinces of Hebei, Shanxi, Inner Mongolia, Shandong, Henan, and Hubei etc. have both high energy saving potentials and high emissions reduction potentials, thus they will play the most important roles in China’s effort on energy conservation and CO2 emissions mitigation. [Copyright &y& Elsevier]

Abstract: This paper presents an overview of some recent CO2 storage capacity assessments in the Songliao and Subei Basins in China, and a discussion of some of the specific challenges that must be addressed to further assess the feasibility of CO2 storage in China. Geological CO2 storage associated with CO2-EOR is of significant interest in these basins as it offers potential to increase oil production and provide additional fuel to meet China’s increasing energy consumption. Although some CO2 is stored during such flooding projects, the greatest storage potential is only realised at the end of a field’s productive life, when a depleted field can be used as a dedicated CO2 storage reservoir. The Songliao Basin, in North-eastern China has been the largest oil and gas producing province in China. The oilfields of the Daqing and Jilin oil provinces are estimated to have effective CO2 storage capacities of 593 Mt and 71.2 Mt respectively. Extrapolation of data from around the oilfields to the whole basin suggests that the most promising saline aquifer in the basin has a theoretical storage capacity of 692 Mt CO2: Its effective storage capacity (which cannot be estimated due to lack of data) is likely to be considerably smaller however. Limited data availability means the estimate of storage capacity in this saline aquifer is based on gross simplifications and extrapolation across large areas (e.g. basin-scale) but could provide a basis for comparison with the potential for CO2 storage in other regions in China, or to help prioritise more detailed analysis. Although smaller, the onshore oilfields of the Subei-Yellow Sea Basin were also assessed, as the presence of mature oil and gas fields and natural CO2 accumulations, suggest suitable targets for storage may be found. The total effective storage capacity of the 108 hydrocarbon fields in this basin are estimated to be only 21 Mt CO2. While small, these fields may offer niche storage options for smaller industrial CO2 sources, but if CCS is to contribute to reducing China’s growing CO2 emissions, then further evaluation and development of storage in saline aquifers is likely to be needed at the same time as storage in oil or gas fields. [Copyright &y& Elsevier]