Abstract:The emergy concept, integrated with a multi-objective linear programming method, was used to model the agricultural structure of Xinjiang Uygur Autonomous Region under the consideration of the need to develop a low-carbon economy. The emergy indices before and after the structural optimization were evaluated. In the reconstructed model, the proportions of agriculture, forestry and artificial grassland should be adjusted from 19:2:1 to 5.2:1:2.5; the Emergy Yield Ratio (1.48) was higher than the average local (0.49) and national levels (0.27); and the Emergy Investment Ratio (11.1) was higher than the current structure (4.93) and that obtained from the 2003 data (0.055) in Xinjiang Uygur Autonomous Region, the Water Emergy Cost (0.055) should be reduced compared to that before the adjustment (0.088). The measurement of all the parameters validated the positive impact of the modeled agricultural structure. The self-sufficiency ratio of the system increased from the original level of 0.106 to 0.432, which indicated a better
OPEN ACCESSEnergies 2011, 4 1429 coupling effect among the subsystems within the whole system. The comparative advantage index between the two systems before and after optimization was approximately 2:1. When the mountain ecosystem service value was considered, excessive animal husbandry led to a 1.41 × 10 10 RMB·a −1 indirect economic loss, which was 4.15 times the GDP during the same time period. The functional improvement of the modeled structure supports the plan to "construct a central oasis and protect the surrounding mountains and deserts" to develop a sustainable agricultural system. Conserved natural grassland can make a large contribution to the carbon storage; and therefore, it is wise alternative that promote a low-carbon economic development strategy.
Climate warming caused by carbon emissions is a hot topic in the international community. Research on urban industrial carbon emissions in China is of great significance for promoting the low-carbon transformation and spatial layout optimization of Chinese industry. Based on ArcGIS spatial analysis, Markov matrix and other methods, this paper calculates and analyzes the temporal and spatial evolution characteristics of industrial carbon emissions in 282 cities in China from 2003 to 2016. Based on the spatial Dubin model, the influencing factors of urban industrial carbon emissions in China and different regions are systematically analyzed. The study shows that (1) China’s urban industrial carbon emissions generally show a trend of first growth and then slow decline. The trend of urban industrial carbon emissions in the western, central, northeastern and eastern regions of China is basically consistent with the overall national trend; (2) In 2003, China’s urban industrial carbon emissions were dominated by low carbon emissions. In 2016, China’s urban industrial carbon emissions were dominated by high carbon emissions, and the spatial trend is gradually decreasing from the eastern region to the central region to the northeast region to the western region; (3) In 2003, the evolution pattern of China’s urban industrial carbon emissions was “low carbon-horizontal expansion” dominated by positive growth, and in 2016, it was “low carbon-vertical expansion” dominated by scale growth; (4) China’s urban industrial carbon emissions have spatial viscosity, and the spatial viscosity decreases with the increase of industrial carbon emissions. (5) In 2004, the relationship between urban industrial carbon emissions and gross industrial output value in China is mainly weak decoupling. In 2016, various types of decoupling regions are more diversified and dispersed, and strong decoupling cities are mainly formed from weak decoupling cities in southwest China and eastern coastal areas; (6) From a national perspective, indicators that are significantly positively correlated with industrial carbon emissions are urban industrial structure, industrial agglomeration level, industrial enterprise scale and urban economic development level, in descending order. Indicators that are significantly negatively correlated with urban industrial carbon emissions are industrial structure and industrial ownership structure, in descending order. Due to the different stages of industrial development and industrial structure in different regions, the influencing factors are also different.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.