• Energy Research
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Abstract The rapid economic development of China has been accompanied by the emission of a great number of pollutants, which in turn have caused severe environmental problems. To strengthen environmental management and to establish a pollution source information database covering all key pollution sources and activities, China carried out its first National Census of Pollution Sources (NCPS) in 2007. The survey contents include the basic environmental situation in 2007, the generation levels of the main pollutants at that time, and the amount of pollution actually discharged into the environment after end-of-pipe treatment at all kinds of pollution sources. Based on the first NCPS report for China released in 2011, and taking two typical industry pollutants, sulfur dioxide (SO 2 ), and chemical oxygen demand (COD) as examples, we first revised the historical data concerning environmental statistics based on the NCPS documents. Subsequently, we analyzed the overall industrial scale in the change of SO 2 and COD emissions using index decomposition analysis, and then studied the contributions and comparative significance of the “three pollution emission reduction measures” put forward by the Chinese government. The latter are: Engineering Emission Reduction (EER), Structure Emission Reduction (SrER) and Supervision Emission Reduction (SuER). From these analyses, we were able to identify the main driving forces for SO 2 and COD emission reduction in China's industrial system. The results indicate that, with continually increasing pollution pressure caused by rapid economic development, EER and SuER have made the greatest contributions to reducing SO 2 and COD emissions; but SrER has not had an obvious effect. In the future, EER and SuER will gradually have less and less potential and become more challenging, while SrER should be achievable through adjusting the economic structure.

As issues concerning “circular economy” and “sustainable development” have been advocated globally, the recycling, reuse, recovery or disposal of end-of-life vehicles (ELVs) have gained more and more attention. Toxic substances and wasted resources within ELVs make evaluating the sustainability of ELV recycling enterprises to be especially important for sound ELV management. In this study, an improved emergy analysis with traditional and revised emergy indices was employed for evaluation of the efficiency and sustainability of ELV recycling enterprises. The constructed emergy indicator system not only reflected the ELV recycling system's resource, environmental, economic and technological efficiency, but also made a multi-scale and multi-dimensional evaluation of the system according to its industrial characteristics. An ELV recycling enterprise in Jiangxi Province, China, as a case, was studied employing the proposed method and indicator system. The results showed that the enterprise involved in the study had strong competitive ability with relatively high economic benefit, and had a moderate environmental impact compared with other industrial systems. The emergy sustainability index indicated that the recycling enterprise was both economically and environmentally sustainable in the long term. However, the emergy technical efficiency indices indicated that the technical level and operational efficiency of the ELV recycling enterprise were relatively low and more investment should be put into the improvement of the recycling technologies. The results of the study can provide useful information for the decision makers to manage ELV recycling enterprises considering not only economic benefits but also the environmental benefits in the long term.

Plants acquire carbon through photosynthesis to sustain biomass production, autotrophic respiration and production of non-structural compounds for multiple purposes. The fraction of photosynthetic production used for biomass production, the biomass production efficiency, is a key determinant of the conversion of solar energy to biomass. In forest ecosystems, biomass production efficiency was suggested to be related to site fertility. Here we present a database of biomass production efficiency from 131 sites compiled from individual studies using harvest, biometric, eddy covariance, or process-based model estimates of production. The database is global, but dominated by data from Europe and North America. We show that instead of site fertility, ecosystem management is the key factor that controls biomass production efficiency in terrestrial ecosystems. In addition, in natural forests, grasslands, tundra, boreal peatlands and marshes, biomass production efficiency is independent of vegetation, environmental and climatic drivers. This similarity of biomass production efficiency across natural ecosystem types suggests that the ratio of biomass production to gross primary productivity is constant across natural ecosystems. We suggest that plant adaptation results in similar growth efficiency in high- and low-fertility natural systems, but that nutrient influxes under managed conditions favour a shift to carbon investment from the belowground flux of non-structural compounds to aboveground biomass.

Abstract The primary problem in achieving carbon emission reduction and carbon peak is to identify the key driving factors and emission reduction potential of the industrial sector, especially in resource-cursed regions like Xinjiang. This study aimed to explore the key driving factors and abatement potential of carbon emissions based on the "energy-environment-economy" hybrid input-output model of Xinjiang during 1997-2017. The result showed that: (1)Industrial carbon emissions have experienced three stages: slow growth-rapid growth-stable growth. (2)Demand change effect and energy intensity effect were the determinants of industrial carbon emission change; Capital formation and domestic trade were the biggest drivers of carbon emissions growth, and the driving force of imports in international trade increased gradually over time. (3) The coal-based energy structure was both the biggest obstacle and the best entry point in carbon emission reduction. (4) Of the 28 key industry sectors, heavy industry including the production and supply of electricity and heat (S22), petroleum processing, coking and nuclear fuel processing (S11), chemical industry (S12), metal smelting and rolling (S14), and energy industries had the greatest potential for carbon reduction. The research findings provide scientific decision-making reference for Xinjiang to accurately grasp the carbon emission reduction potential of the industry and formulate a targeted carbon peak action plan.

Cities are perhaps one of the most challenging and yet enabling arenas for sustainable development goals. The Sustainable Development Goals (SDGs) emphasize the need to monitor each goal through objective targets and indicators based on common denominators in the ability of countries to collect and maintain relevant standardized data. While this approach is aimed at harmonizing the SDGs at the national level, it presents unique challenges and opportunities for the development of innovative urban-level metrics through big data innovations. In this article, we make the case for advancing more innovative targets and indicators relevant to the SDGs through the emergence of urban big data. We believe that urban policy-makers are faced with unique opportunities to develop, experiment, and advance big data practices relevant to sustainable development. This can be achieved by situating the application of big data innovations through developing mayoral institutions for the governance of urban big data, advancing the culture and common skill sets for applying urban big data, and investing in specialized research and education programs.

Nighttime light data is a mainstay method in confirming and supporting other traditional economic data indicators, which in turn influence business and policy-making decisions. Accuracy in and clear definition of economic data and its related indicators are thus of great importance not only for analysis of urban development and related policies seeking sustainable development, but also plays a key role in whether or not these policies are successful. Discovering and recognizing the applications and limitations of nighttime light and other peripheral data could prove helpful in future data analysis and sustainable development policy. One possible limitation could exist in GDP decrease, and whether or not nighttime light would decrease accordingly, as most studies show nighttime light increase confirms economic growth, thus affecting the reliability of the data’s correlation with economic data. This study utilizes nighttime light data in a cross-reference with GDP data during instances of global GDP shrinkage over the years of 2007–2017, split between 2007–2012 for the DMSP dataset and 2013–2017 for the VIIRs dataset. It seeks to establish through linear regression whether or not yearly average nighttime light data products show a positive correlation even during periods of economic decline, thereby providing a clearer understanding of the strengths and limitations of NTL as an economic validation indicator. Analysis shows that both years of global GDP decrease in turn also displayed global nighttime light decrease, in addition to linear regression giving satisfactory results pointing to a positive correlation over the timespan. The VIIRS data series resulted in higher regression coefficients, which is in line with the results of previous literature.