Spatial-Temporal Hotspot Pattern Analysis of Provincial Environmental Pollution Incidents and Related Regional Sustainable Management in China in the Period 1995–2012

Abstract

Spatial-temporal hotspot pattern analysis of environmental pollution incidents provides an indispensable source of information for the further development of incident prevention measures. In this study, the spatial-temporal patterns of environmental pollution incidents in China in the period of 1995-2012 were analyzed, using the Spatial Getis-Ord statistic and an Improved Prediction Accuracy Index (IAPI). The results show that, in this period, the occurrence of environmental incidents exhibited a dynamic growth pattern but then dropped and continued to drop after the year 2006, which was considered a crucial turning point. Not coincidentally, this corresponds to the year when the State Council issued its National Environmental Emergency Plan, and following the examination of major incidents, special actions were taken to strengthen the control of incidents and emergency responses. The results from Getis-Ord General G statistical analysis show that the spatial agglomeration phenomenon was statistically significant after 1999 and that the level of spatial agglomeration was rising, while the Getis-Ord Gi * statistical analysis reveals that environmental pollution incidents were mainly agglomerated in the Pan Yangtze River OPEN ACCESSSustainability 2015, 7 14386Delta and Pan Pearl River Delta regions. Accordingly, the spatial-temporal hotspot pattern based on the IAPI values at the provincial scale could be categorized into: stable hotspots, unstable hotspots, and cold-spot areas. The stable hotspots category was further divided into three subtypes: industrial distribution type, industrial transfer type, and extensive economic growth type. Finally, the corresponding measures for sustainable management were proposed: stable hotspots were classified as essential regions requiring the immediate prevention and control of environmental pollution incidents; unstable hotspots were characterized by their need for ongoing and continual prevention measures, and cold-spots were those areas that required strengthened environmental monitoring. Meanwhile, it was identified that a multi-level environmental pollution incident emergency response and regional (incident) joint control plan needed to be well prepared and carried out effectively. To prevent environmental pollution and the regional transfer of pollution from incidents, measures towards achieving regional environmental planning, environmental risk prevention, environmental emergency monitoring and adequate emergency responses must be efficiently implemented.

An Assessment of the Impact of Spatial Agglomeration on the Quality of China’s Wood Processing Industry Products

“…where MC is the marginal variable cost, F is the fixed cost, ϕ and ξ are enterprise productivity and fixed cost efficiency, respectively, c and f are the variable and fixed input prices, respectively, and β and α are the quality elasticity of variable and fixed costs, respectively. According to Equations (2) and (3), the optimal export product quality is achieved by profit maximization:…”

“…The "low price and high quantity" export growth mode also means that Chinese wood processing enterprises are threatened by "low-end lock in the value chain" [2]. Therefore, to improve the international competitiveness of China's wood processing industry, a shift from the traditional price competition to the competition of quality is urgently needed [3]. As for industrial distribution, there are five major wood panel industry clusters in quality was described in the second section; an empirical analysis of agglomeration and product quality was provided in the third section; we discussed our results in the fourth section; and finally, we described our conclusions and the implications of this study in the fifth section.…”

Using spatial statistics to identify emerging hot spots of forest loss

“…Spatial pattern analysis, while not inherently predictive, also has the potential to assist in rapid, consistent identification of priority areas for management intervention. For example, statistical methods have been used to identify trends in biodiversity (Myers 1988, Mittermeier 1998, Myers et al 2000, pollution (Yong-Hui et al 2010, Li et al 2014, Ding et al 2015, and crime (Grubesic 2006, Chainey et al 2008, Xiaoland and Grubesic 2010, Sangamithra et al 2012. In the context of forest conservation, spatial statistics can assist in quickly identifying spatiotemporal trends of forest loss without the explicit need for pre-existing information on what underlying factors are driving these trends.…”

Environmental incidents in China: Lessons from 2006 to 2015

“…All these studies have enhanced our understanding of the nature of the problems related to environmental incidents. In particular, statistical methods have been widely employed in these studies (Glickman and Golding, 1992;Shin, 2013;Uth, 1999).With regard to China's environmental incidents, these studies have mainly concentrated on the analysis of characteristics (Ding et al, 2015;Hou and Zhang, 2009;Lu et al, 2012;Yao et al, 2016), the evaluation of damage loss (Li et al, 2008;Xue and Zeng, 2011) as well as the evaluation of influencing factors (Li et al, 2008;Yang et al, 2013) based on the public statistics. Some studies have focused on a specific type of material or sector, such as industrial incidents (Chan et al, 2015;Wei and Lu, 2015), hazardous chemical incidents (Duan et al, 2011;He et al, 2011;Zhang and Zheng, 2012), and traffic incidents (Yang et al, 2010).…”