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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.

With the Rise of Central China Plan, the central region has had a great opportunity to develop its economy and improve its original industrial structure. However, this region is also under pressure to protect its environment, keep its development sustainable and reduce carbon emissions. Therefore, accurately estimating the temporal and spatial dynamics of CO2 emissions and analysing the factors influencing these emissions are especially important. This paper estimates the CO2 emissions derived from the fossil fuel combustion and industrial processes of 18 central cities in China between 2000 and 2014. The results indicate that these 18 cities, which contain an average of 6.57% of the population and 7.91% of the GDP, contribute 13% of China's total CO2 emissions. The highest cumulative CO2 emissions from 2000 to 2014 were from Taiyuan and Wuhan, with values of 2268.57 and 1847.59 million tons, accounting for 19.21% and 15.64% of the total among these cities, respectively. Therefore, the CO2 emissions in the Taiyuan urban agglomeration and Wuhan urban agglomeration represented 28.53% and 20.14% of the total CO2 emissions from the 18 cities, respectively. The three cities in the Zhongyuan urban agglomeration also accounted for a second highest proportion of emissions at 23.51%. With the proposal and implementation of the Rise of Central China Plan in 2004, the annual average growth rate of total CO2 emissions gradually decreased and was lower in the periods from 2005 to 2010 (5.44%) and 2010 to 2014 (5.61%) compared with the rate prior to 2005 (12.23%). When the 47 socioeconomic sectors were classified into 12 categories, “power generation” contributed the most to the total cumulative CO2 emissions at 36.51%, followed by the “non-metal and metal industry”, “petroleum and chemical industry”, and “mining” sectors, representing emissions proportions of 29.81%, 14.79%, and 9.62%, respectively. Coal remains the primary fuel in central China, accounting for an average of 80.59% of the total CO2 emissions. Industrial processes also played a critical role in determining the CO2 emissions, with an average value of 7.3%. The average CO2 emissions per capita across the 18 cities increased from 6.14 metric tons in 2000 to 15.87 metric tons in 2014, corresponding to a 158.69% expansion. However, the average CO2 emission intensity decreased from 0.8 metric tons/1000 Yuan in 2000 to 0.52 metric tons/1000 Yuan in 2014 with some fluctuations. The changes in and industry contributions of carbon emissions were city specific, and the effects of population and economic development on CO2 emissions varied. Therefore, long-term climate change mitigation strategies should be adjusted for each city.

China announced at the Paris Climate Change Conference in 2015 that the country would reach peak carbon emissions around 2030. Since then, widespread attention has been devoted to determining when and how this goal will be achieved. This study aims to explore the role of China’s changing regional development patterns in the achievement of this goal. This study uses the logarithmic mean Divisia index (LMDI) to estimate seven socioeconomic drivers of the changes in CO2 emissions in China since 2000. The results show that China’s carbon emissions have plateaued since 2012 mainly because of energy efficiency gains and structural upgrading (i.e., industrial structure, energy mix and regional structure). Regional structure, measured by provincial economic growth shares, has drastically reduced CO2 emissions since 2012. The effects of these drivers on emissions changes varied across regions due to their different regional development patterns. Industrial structure and energy mix resulted in emissions growth in some regions, but these two drivers led to emissions reduction at the national level. For example, industrial structure reduced China’s CO2 emissions by 1.0% from 2013-2016; however, it increased CO2 emissions in the Northeast and Northwest regions by 1.7% and 0.9%, respectively. By studying China’s plateauing CO2 emissions in the new normal stage at the regional level, it is recommended that regions cooperate to improve development patterns.

An optimization model is developed based on the Input–Output model to assess the potential impacts of industrial structure on the energy consumption and CO2 emission. The method is applied to a case study of industrial structure adjustment in Beijing, China. Results demonstrate that industrial structure adjustment has great potential of energy conservation and carbon reduction. When the average annual growth rate of GDP is 8.29% from 2010 to 2020, industrial structure adjustment can save energy by 39.42% (50.06 million tons of standard coal equivalent), and reduce CO2 emission by 46.06% (96.31 million tons) in Beijing in 2020. Second, Beijing had better strive to develop several low energy intensive and low carbon intensive sectors, such as information transmission, computer service and software, and finance. Third, energy intensity is possible to decrease without negatively affecting economic growth by reasonable industrial structure adjustment. Four, compared to “intensity targets”, “total amount targets” are more effective on the energy conservation and carbon reduction, but have much greater negative effects on economic growth. Therefore, it needs to be balanced between “total amount targets” and “intensity targets”.

“The Low Carbon, Green Growth” was declared as Korean national agenda in 2008. Korea has been enhancing the green growth for the sustainable economic development and fostering energy. To improve Korean national energy security and promote the “Low Carbon, Green Growth”, we established a long term strategic energy technology roadmap. In this paper, five criteria, such as economical impact, commercial potential, inner capacity, technical spin-off, and development cost, were used to assess the strategic energy technologies against high oil prices. We developed the integrated two-stage multi-criteria decision making (MCDM) approach which was used to evaluate the relative weights of criteria and measures the relative efficiency of energy technologies against high oil prices. On the first stage, the fuzzy analytic hierarchy process, reflecting the vagueness of human thought with interval values instead of crisp numbers, allocated the relative weights of criteria effectively instead of the AHP approach. On the second stage, the data envelopment analysis approach measured the relative efficiency of energy technologies against high oil prices with economic viewpoints. The relative efficiency score of energy technologies against high oil prices can be the fundamental decision making data which help decision markers to effectively allocate the limited R&D resources.

Abstract Primary energy requirements have close interaction with resource, technology, environment, infrastructure, as well as the socio-economic development. This study links the entire supply chain of the Chinese economy from energy extraction to final consumption by using input-output analysis and structural path analysis. The results show that the domestic primary energy input amounted to 3318.7 Mtce in 2012, of which 49.5% was induced by investment demands. Despite being one of the world's largest energy importers, embodied energy uses (EEUs) in China’s exports were equivalent to about one fourth of its total domestic supply. All Manufacturing sectors accounted for 44.3% of the total EEUs, followed by Construction for 33.3%, Services for 11.6% and Power & Heat for 3.9%. After examining the embodied energy paths, critical economic sectors such as Construction of Buildings, Construction Installation Activities, Transport Via Road, Production and Supply of Electricity and Steam and Processing of Steel Rolling Processing, and supply chain routes starting from final uses to resource extraction such as “Capital formation → Construction of Buildings → Production and Supply of Electricity and Steam → Production and Supply of Electricity and Steam → Mining and Washing of Coal”, were identified as the main contributors to China’s raw coal and other primary energy requirements. Restructuring Chinese economy from manufacturing industries to construction and services with huge economic costs cannot fundamentally conserve energy, owing to their almost identical structures in higher production tiers; more appropriate policies on technology efficiency gains, energy mix improvement, economic structure adjustment and green consumption deserve to be considered in the light of upstream and downstream responsibilities from a systematic viewpoint.