Study on the After-evaluation Index System and Evaluation Model of Industrial Estate Land Development Project

According to the modem management theories and the characters of industrial estate land development project, this paper studies the after-evaluation＇s content and method of industrial estate land development project and puts forward the index system of course after-evaluation, finance after-evaluation and society after-evaluation. Moreover, this paper also brings forward the method and model of synthetic after-evaluation.

Calculation and Analysis of the Industrial Green Development Index in Hunan Province

In order to realize the purpose of human sustainable development,green development has become a dominant trend in contemporary society. ＂ Green Hunan＂ is the primary slogan of Hunan＇s four cards. Thus,in recent years,what is the practice result of Hunan＇s green development？ Based on the concept of industrial green development index,we selected the related panel data from 14 different cities of Hunan Province in 2012. The energy consumption and carbon emissions were also selected as input indexes,and GDP of secondary industry was used as the output index to build the industrial green development index. The CRS model of DEA was used to calculate and sequence the industrial green development indexes of 14 cities in Hunan Province.

Spatial-temporal Pattern Evolution of Manufacturing Geographical Agglomeration and Influencing Factors of Old Industrial Base:A Case of Jilin Province,China

The primary object of this paper is to examine the spatial-temporal pattern evolution of manufacturing geographical agglomeration of the old industrial base.Industrial spatial agglomeration index and concentration ratio are used in this paper.Multiple linear regression models are also applied to try to explore the internal driving mechanisms on manufacturing geographical agglomeration.The results show that:1) the manufacturing agglomeration degree of Jilin Province is increasing gradually.The spatial polarization structure is visible;and the central region is the agglomeration area,in addition,the manufacturing industries of Changchun Proper present a trend of dispersion;2) the structure of manufacturing industries has changed,and the concentration ratio of labor-intensive manufacturing industry is declining,while the proportions of technology-intensive and capital-intensive manufacturing industry are relatively rising;3) marketing level,location accessibility,labor resources,capital,science and technology innovation capability,scale economy and the level of globalization affect manufacturing agglomeration with different degree.There are significant differences of the effects about employment,technology,the quality of residents and the export-oriented market on the industrial concentration ratio;4) in the future,the impact of policy and institution,export-oriented market and quality of resident on manufacturing geographical agglomeration pattern will be more profound.

Industry return lead‑lag relationships between the US and other major countries

In this study,we analyze the lead-lag relationships between the US industry index and those of six other major countries from January 1973 to May 2021.We identify the leading role played by the US internationally by showing that the weekly returns of US industries,especially the US basic materials and energy industries,significantly Granger cause the returns of most other countries’industries,suggesting that non-US industries react with some delay to new information.This delayed reaction is even more notice-able during periods of recession in the US when cross-country correlations are higher.This implies that the ability of the lagged returns of US industries to predict industries’returns from other countries is even more pronounced when the US experienced an economic recession.A similar asymmetric relationship is found between the volatil-ity of US industries and that of industries in other markets.The analysis of causality in the distribution of returns and volatility shows that causality runs mainly from the US to other countries,particularly in the presence of extreme negative shocks.Finally,we demonstrate that our predictions are valuable to real-world investors.Long-short strategies generate sizable and statistically significant alphas,and a constant relative risk-averse investor obtains certainty equivalent returns well above the risk-free rate.

Evolution of Regional Industrial Structure and Characteristics of Pollution Emissions in China

We conducted a quantitative analysis of the relationship between the evolution of China’s industrial structure and environmental change. Using the industrial structure characteristic bias index and data from 29 manufacturing industries in each province from 1995 to 2010, we described the impact of pollution-emission characteristics of industry structure on the regional environment. The results show that wastewater-discharged characteristic of industrial structure in the northwest and central areas declined from 1995-2010, but this trend has weakened. The industrial structure of southwest and southeast areas had a high level of solid waste discharge and a constantly strengthening trend in SO2 emissions. We also analyzed the growth and recession of each sector in eight regions in China from 1995 to 2010. Among the eight regions, the middle reach of the Yel ow River, and northwest and southwest China had a clear increasing trend of high pol ution industries;the eastern coast, southern coast, middle reach of the Yangtze River and northeastern China did not sustain an increase in high pollution industries. In addition, regression analysis modeling was used to analyze the impact of the evolution of industrial structure on the disparities of regional environmental effects. We found that regional industry structure was closely related to environmental quality, especially for western areas at lower stages of economic development. In the future, all regions must prevent and forefend the risk of environmental pollution in the update and adjustment of industrial structure.

Analysis of Air Pollution Impact Factors in China： A MIMIC Modeling Approach

In this study, we investigate the impact factors on air pollution in terms of CO2, SO2 and NOx emissions simultaneously in China and compare changes in air pollution across provinces from 1998 to 2011 using a Multiple Indicators and Multiple Causes Model （MIMIC） within a Structural Equation Model （SEM） framework. Our findings reveal that GDP per capita and total population have the largest impacts on air pollution, followed by energy intensity, foreign direct investment, population density, and industrialization. The results also reveal that the inverted U-shaped Environmental Kuznets Curve （EKC） hypothesis exists in China. Our findings also demonstrate that Shandong, Jiangxi and Liaoning are the top three provinces with the most deteriorated air quality while Xinjiang, Fujian and Ningxia are with the best. These results not only contribute to advancing the existing literature, but also merit particular attention from policy-makers in China.