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Environmental Regulation And Green Technical Efficiency Of Water

Posted on:2017-10-22Degree:MasterType:Thesis
Country:ChinaCandidate:Y ZhangFull Text:PDF
GTID:2311330512974617Subject:Economics of Regulation
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China is rich in natural resources,which not only ensure the adequacy inputs in the process economic development but also restrict the pace to intensive mode of economic growth.With the rapid economic development,environmental pollution problem is becoming more and more serious,and environmental pollution accidents happen frequently.This kind of development mode that expense resources for development is inconsistent to the concept of sustainable development.Considering the environmental capacity with attribute of public goods and environmental pollution with negative externality,market mechanism has been inefficient.However,government behavior is indispensable in the process of resource saving and environmental protection.To achieve the development goal of the economic and ecological balance,it is particularly important to build a performance indicator that can reflect the economic benefit and ecological benefit at the same time.Then we need to analyze the internal mechanism how environmental regulations affect the indicator.The utilization of water resources,related to human production and life,has attracted more and more attention.Therefore,this article takes water as a representative and analyzes the situation of water using and pollution in the field of industry.This paper found that there are big geographic differences on the situation of pollution,and it is being improved on the time dimension,but the integral level is still on the high side.After comparative analysis of a variety of method,based on data envelopment analysis(DEA),to reflect efficiency and ultimately selected slack-based direction distance function to measure green technical efficiency of industrial water of 30 provinces(except Tibet)in China mainland comprehensively.The directional vector norm is constrained to the observed value norm in this method,which is better for reflecting the scale economies and reducing the subjective in the process of directional vector selection.We choose industrial water,industrial investment in fixed assets and industrial practitioners as input variables,the industrial added value as expected output,industrial chemical oxygen demand(cod)and industrial sources of ammonia nitrogen as the unexpected output.The result shows that China's green technical efficiency of industrial water is low as a whole,but the index is improved in most of the provinces in vertical comparison.There are significant differences in the efficiency of provinces,and the green technical efficiency of industrial water in eastern China is generally higher than the province is located in the western region.The reason is that provinces in eastern region are more developed compared to the west,and the economic development level and technology level are superior to the west.Improve water conservancy facilities and more pollution abatement investment are also beneficial to the sustainable utilization of water resources and achieve higher efficiency of green technology.We found that the index has spatial positive correlation and spatial agglomeration after calculating the efficiency of industrial water green technology of global autocorrelation index(global Moran's I)and partial autocorrelation index(local Moran's I).Finally,the spatial autoregressive model is chosen for regression analysis.Green technical efficiency of industrial water is set as dependent variable,and environmental regulation intensity is set as the main explanatory variables,with the economic development level,abundance of water resources,education investment,enterprise scale,foreign direct investment,R&D and sewage treatment as control variables.In addition to test the existence of the porter hypothesis in terms of industrial water in China,we also introduce the quadratic term of environmental regulation intensity into the model.We describe environmental regulation intensity from four points:legal system,method system,supervision system and support system,then we hire entropy value method to calculate the intensity of environmental regulation.Regression analysis result shows that intensity of environmental regulation has effect on green technical efficiency of industrial water and the shape of curve between them is "U",which means green technical efficiency is rising and then falling along with the increase of environmental regulation intensity,and when the regulation intensity is around 0.7,the turning point of the U-shape curve can be find.At present,Beijing,Hebei,Shanxi,Liaoning,Heilongjiang,Guangxi,Chongqing and Ningxia province environmental regulation intensity can break through the threshold level,but most provinces in China cannot.As for control variables,the level of economic development,education investment,foreign direct investment,enterprise R&D and sewage treatment capacity of industrial water efficiency have a significant positive influence on green technical efficiency of industrial water,and the influence of water abundance and the enterprise scale are not very significance.Finally,we divide all provinces into three parts(eastern area,middle area,west area)and then use regression analysis separately.Results show that the relation between environmental regulation intensity and green technical efficiency is U-shape curve only in eastern area and in middle area.Finally,from the standpoint of sustainable development,we put forward that the governments should increase the intensity of environmental regulation and strive for an early breakthrough the turning level of environmental regulation intensity.Governments also should establish green GDP tournament mechanism,make full use of the spatially positive interaction among provinces,integrate resources,share information,pay attention to technology innovation,design reasonable incentive mechanism,and attract more private capital into the technology innovation.
Keywords/Search Tags:Green technical efficiency, Environmental regulation, Direction distance function, Spatial econometric model
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