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This paper analyzes panel data from 2003–2012 to identify the factors driving the expansion of construction land in Ningbo city; it uses panel data, regional-level, and year-by-year regression models. The results indicate the following: (1) For each 1% increase in the size of the economy, urban population, and industrial structure adjustment coefficient, the amount of construction land increased by 0.35%, 0.52% and -1%, respectively. (2) The factors driving the expansion of urban construction land differed across regions. In more highly developed areas such as Yuyao, Cixi, Fenghua and the downtown area, population growth was the most obvious driving factor with coefficients of 4.880, 1.383, 3.036 and 0.583, respectively, in those areas. Here, the impact of industrial structure adjustment was lower than that of population growth (with coefficients of 1.235, 0.307, 0.145 and -0.242), while economic development was an increasingly insignificant factor (with coefficients of -0.302, 0.071, 0.037 and 0.297). On the other hand, economic development was the most important factor for the expansion of construction land in relatively less developed areas such as Xiangshan and Ninghai counties with coefficients of 0.413 and 0.195, respectively. Here, population growth (with coefficients of -0.538 and 0.132) and industrial structure adjustment (with coefficients of -0.097 and 0.067) were comparatively weaker driving factors. (3) The results of the year-by-year regression indicate the increased impact of economic development as a driving factor (from -1.531 in 2005 to 1.459 in 2012). The influence of the population growth factor slowly declined (from 1.249 in 2005 to 0.044 in 2012) and from 2009 on was less influential than the economic development factor. The industrial structure coefficient remained negative and its influence diminished from year to year (from -5.312 in 2004 to -0.589 in 2012).
Mou Chufu; Wang Limao; Qu Qiushi; Fang Yebing; Zhang Hong. Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China. Journal of Resources and Ecology 2018, 9, 365 -373.
AMA StyleMou Chufu, Wang Limao, Qu Qiushi, Fang Yebing, Zhang Hong. Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China. Journal of Resources and Ecology. 2018; 9 (4):365-373.
Chicago/Turabian StyleMou Chufu; Wang Limao; Qu Qiushi; Fang Yebing; Zhang Hong. 2018. "Factors Driving the Expansion of Construction Land: A Panel Data Study of Districts and Counties in Ningbo City, China." Journal of Resources and Ecology 9, no. 4: 365-373.
宏 张; Zhang Hong; 礼茂 王; 英卓 张; 初夫 牟; 叶兵 方; 慧敏 杨; Wang Limao; Zhang Yingzhuo; Mou Chufu; Fang Yebing; Yang Huimin. 低碳经济背景下中国风力发电跨区并网研究. 资源科学 2017, 39, 2377 -2388.
AMA Style宏 张, Zhang Hong, 礼茂 王, 英卓 张, 初夫 牟, 叶兵 方, 慧敏 杨, Wang Limao, Zhang Yingzhuo, Mou Chufu, Fang Yebing, Yang Huimin. 低碳经济背景下中国风力发电跨区并网研究. 资源科学. 2017; 39 (12):2377-2388.
Chicago/Turabian Style宏 张; Zhang Hong; 礼茂 王; 英卓 张; 初夫 牟; 叶兵 方; 慧敏 杨; Wang Limao; Zhang Yingzhuo; Mou Chufu; Fang Yebing; Yang Huimin. 2017. "低碳经济背景下中国风力发电跨区并网研究." 资源科学 39, no. 12: 2377-2388.
初夫 牟; Mou Chufu; 礼茂 王; 秋实 屈; 叶兵 方; 宏 张; Wang Limao; Qu Qiushi; Fang Yebing; Zhang Hong. 主要新能源发电替代减排的研究综述. 资源科学 2017, 39, 2323 -2334.
AMA Style初夫 牟, Mou Chufu, 礼茂 王, 秋实 屈, 叶兵 方, 宏 张, Wang Limao, Qu Qiushi, Fang Yebing, Zhang Hong. 主要新能源发电替代减排的研究综述. 资源科学. 2017; 39 (12):2323-2334.
Chicago/Turabian Style初夫 牟; Mou Chufu; 礼茂 王; 秋实 屈; 叶兵 方; 宏 张; Wang Limao; Qu Qiushi; Fang Yebing; Zhang Hong. 2017. "主要新能源发电替代减排的研究综述." 资源科学 39, no. 12: 2323-2334.
叶兵 方; Fang Yebing; Wang Limao; Mou Chufu; Zhang Hong; Qu Qiushi. 中国石油终端利用碳排放空间分异及影响因素. 资源科学 2017, 39, 2233 -2246.
AMA Style叶兵 方, Fang Yebing, Wang Limao, Mou Chufu, Zhang Hong, Qu Qiushi. 中国石油终端利用碳排放空间分异及影响因素. 资源科学. 2017; 39 (12):2233-2246.
Chicago/Turabian Style叶兵 方; Fang Yebing; Wang Limao; Mou Chufu; Zhang Hong; Qu Qiushi. 2017. "中国石油终端利用碳排放空间分异及影响因素." 资源科学 39, no. 12: 2233-2246.
Understanding the spatial heterogeneity and driving force identification of energy-related CO2 emissions (ECEs) can help build consensus for mitigating CO2 emissions and designing appropriate policies. However, previous studies on ECEs that focus on both the global-regional scale and the interaction of factors have been seldom conducted. In this paper, ECE data from 143 countries from 1990 to 2014 were selected to analyze regional differences in ECE growth rates by using the coefficient of variation. Then a geographical detector was used to analyze the key determinant factors on ECE growth rates around the world and in eight types of regions. The results show that: (1) the ECE growth rate in the Organization for Economic Cooperation and Development (OECD) region is low and tended to decrease, while in the non-OECD region it is high and tended to increase; (2) the coefficient of variation and detection factor of ECE growth rates at a regional scale are higher than those at a global scale; (3) in terms of the key determinant factors, population growth rate, growth rate of per capita GDP, and energy intensity growth rate are the three key determinant factors of ECE growth rates in the OECD region and most of the non-OECD regions such as non-OECD European and Eurasian (NO-EE), Asia (NO-AS), non-OECD Americas (NO-AM). The key determinant factors in the African (NO-AF) region are population growth rates and natural gas carbon intensity growth rates. The key determinant factors of the Middle East (NO-ME) are population growth rate, coal carbon intensity growth rate and per capita GDP growth rate; (4) the determinant power of the detection factor, the population growth rate at the global scale and regional scale is the strongest, showing a significant spatial consistency. The determinant power of per capita GDP growth rate and energy intensity growth rate in the OECD region, respectively, rank second and third, also showing a spatial consistency. However, the carbon intensity growth rates of the three fossil fuels contribute little to the growth rate of ECEs, and their spatial coherence is weak; (5) from the perspective of the interaction of detection factors, six detection factors showed bilinear or non-linear enhancement at a global and a regional scale, and the determinant power of the interaction of factors was significantly enhanced; and (6) from the perspective of ecological detection, the growth rate of carbon intensity and the growth rate of natural gas carbon intensity at the global scale and NO-ME region are significantly stronger than other factors, with a significant difference in the spatial distribution of its incidence. Therefore, the OECD region should continue to reduce the growth of energy intensity, and develop alternative energy resources in the future, while those that are plagued by carbon emissions in non-OECD regions should pay more attention to the positive influence of lower population growth rates on reducing the growth...
Yebing Fang; Limao Wang; Zhoupeng Ren; Yan Yang; Chufu Mou; Qiushi Qu. Spatial Heterogeneity of Energy-Related CO2 Emission Growth Rates around the World and Their Determinants during 1990–2014. Energies 2017, 10, 367 .
AMA StyleYebing Fang, Limao Wang, Zhoupeng Ren, Yan Yang, Chufu Mou, Qiushi Qu. Spatial Heterogeneity of Energy-Related CO2 Emission Growth Rates around the World and Their Determinants during 1990–2014. Energies. 2017; 10 (3):367.
Chicago/Turabian StyleYebing Fang; Limao Wang; Zhoupeng Ren; Yan Yang; Chufu Mou; Qiushi Qu. 2017. "Spatial Heterogeneity of Energy-Related CO2 Emission Growth Rates around the World and Their Determinants during 1990–2014." Energies 10, no. 3: 367.