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Urbanization becomes the primary source of carbon emissions and energy demand growth in China with industrialization entering the middle-later stages. With the current highconsumption and high-emission urban development model, the resulting resource consumption and carbon emissions become a major environmental issue and a sustainability problem. Based on the panel data of the bay area economic zone of Zhejiang province from 2003 to 2017, this paper analyzed the overall spatial and temporal distribution characteristics of carbon emissions of the economic zone. The main sources of carbon emissions will be studied in detail by incorporating the urbanization level, energy consumption structure, land use, economic development level, industrial structure, population agglomeration, openness, and technological progress into the STIRPAT (the Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model. We further investigated the driving mechanism of carbon emissions and spatial and temporal heterogeneity of carbon emissions from the perspective of urbanization using the geographically and temporally weighted regression (GTWR). The results showed that: (1) The scale of carbon emissions has increased with time, but the growth rate has not been large with a decreasing growth rate. Carbon emissions in different regions are significantly different. The carbon emission is less intense through the years with a spatial distribution pattern of increasing from the southwest to the northeast. (2) The level of economic development and the degree of openness to the outside world are the dominant factors that affect the carbon emission level, followed by energy consumption structure, technological progress, and urbanization. (3) These influencing factors show substantial spatiotemporal heterogeneity that the direction and intensity of the fluctuations of various driving factors vary from time to time and region to region. This paper analyzed the impact of the driving factors of carbon emissions in the economic zone of Zhejiang Bay Area explicitly, providing scientific support for the implementation of regionally differentiated carbon emission reduction strategies. It is necessary to fully examine the actual development of each region to formulate a differentiated carbon emission reduction regulation strategy. In particular, these measures should be taken into consideration for reducing carbon emissions in the area: enhancing the efficiency of comprehensive utilization of resources by accelerating the construction of science and technology innovation corridors, and promoting the transformation and upgrading of traditional industries by fully introducing modern service industries and high-end manufacturing based on information technology. [ABSTRACT FROM AUTHOR]

The national economy is a complex system composed of many different industries and interdependent industries. Scientifically identifying the correlation conduction structure of industrial carbon emissions is of great theoretical and practical significance for achieving the goal of carbon emission reduction in China. Therefore, the total carbon footprint was defined from the direct and indirect angles. Based on the energy consumption and the direct consumption coefficient of production service, the admissibility of the function improvement was created to update the input-output table and ram the data source base. Then the balanced input output industrial total carbon footprint life cycle model was designed to calculate the total carbon footprint of the industry. Using the WT index method, China's industry total carbon footprint complex networks in the new and old normal periods were constructed, and the characteristics of networks were analyzed from the point of view of the whole network, nodes status and community structure. By comparison, it was found that the structures of China's industrial total carbon footprint complex networks in the new and old normal periods were reasonable. Compared with the old normal period, the total industrial carbon footprint in the new normal period industry linked closer, the core key industries had stronger control influence, and the community structure was generally stable. At last, the paper put forward countermeasures and suggestions, which were a comprehensive consideration of the total carbon footprint of the industry, taking into account the individual and the overall characteristics, focusing on the key nodes and taking full consideration of the characteristics of the industrial community. And we hope this paper could improve the efficiency of China's industrial synergistic emission reduction in the new normal period. [ABSTRACT FROM AUTHOR]

Since the reform and opening up, the urbanization rate in China has risen about 40% from 1978 to 2016, and it has been rapidly advancing at a rate of 0.5 percentage points higher than the world average each year. In the process of city booming, there inevitably exist problems such as extensive land use, urban sprawl and excessive energy consumption. In recent years, environmental pollution such as haze caused by excessive energy consumption has impacted seriously on the life quality of urban residents, and the problem is even more serious in large cities. In western countries, it has been proved that compact city is an effective development mode in alleviating energy shortage and environmental pollution in large cities. However, the concept of compact city has not been given enough attention in China. In this paper, we focused on the relationship between energy efficiency and compactness. The energy efficiency and compactness of 29 large cities during 2003- 2014 were measured with superefficiency- DEA model and entropy value method, and then quadrant map approach was introduced to explore the relationship between urban compactness and energy efficiency. The results showed that energy efficiency was relatively low but was increasing slowly. The regional disparities of the relationship between urban compactness and energy efficiency were significant. The eastern part of China mainly had high coordination relationship, while the middle and western part of China mostly had low coordination relationship. From the perspective of the scale of cities, larger cities had higher energy efficiency. As the city size increased, the proportion of high coordination type became higher and higher. Megacities mostly fell into the categories of high coordination, and small cities mostly fell into the categories of low coordination. The quadrant diagram analysis after eliminating other possible affecting factors showed more obvious result that more compact city had higher energy efficiency. To improve the energy efficiency, multi-functional composite utilization of economy humanity and ecology should be realized by putting more investment into the construction of public service facilities and infrastructure, intensifying industrial land use and perfecting the functional system of cities. [ABSTRACT FROM AUTHOR]