Economic Impacts and Emergency Management of Disasters in China

As global climate warms, the occurrence frequency and loss of natural disaster are both increasing, posing a great threat to the sustainable development of human society. One of the most important approaches of disaster management is to prevent disaster and reduce disaster loss through fiscal expenditure of government; however, the optimal proportion of expenditure for disaster prevention and mitigation has always been a difficult issue that people concern about. First, this paper, after considering the impact of disaster on human capital, established a resident-manufacturer-government decision making model which contains the probability of disaster, and then solved the optimal proportion of government expenditure for disaster prevention and reduction as well as the expected economic growth rates under different conditions. Second, through numerical simulation method, this paper studied the impacts of such factors as coefficient of risk aversion and elasticity coefficient of substitution on the optimal proportion of disaster prevention and reduction expenditure. Third, through constant elasticity of sub-situation (CES) production function and ridge regression method, this paper verified the applicability of the proposed model with the data of the expenditures for disaster prevention and mitigation of Hunan Province in 2014. Finally, this paper summarized the research results and put forward corresponding suggestions on policy. The theoretical model proposed in this paper enriches the related researches of disaster economics, and the conclusions of empirical analysis can provide government departments with useful reference for the practice of disaster prevention and mitigation.

With the acceleration of industrialization and urbanization, the problem of air pollution in China has become increasingly serious. Particulate matter (PM) is a representative indicator of pollutants, and it is of great significance to carry out targeted treatment by studying its periodicity of concentration. In this paper, as a data mining information technology, the Empirical Mode Decomposition-Wavelet Analysis (EMD-WA) model is used to conduct a multi-scale periodic study of the PM_2.5 concentration time sequence in the Yangtze River Delta region in China and it is found that: (1) through the decomposition and reconstruction of the EMD-WA model, the period characteristics of four scales from short to long can be obtained, which are seasonal, short, medium and long period terms respectively; (2) the PM_2.5 concentration in the Yangtze River Delta region shows obvious multi-scale periodicity for the four scales, which includes a seasonal cycle of 46 days (about 1.5 months), a short cycle of 101 days (about 3.5 months), a medium cycle of 294 days (about 10 months), and a long cycle of 671 days (about 22.5 months), respectively. (3) The results are consistent in terms of season, short and middle cycle scales, in north (Jiangsu), east (Shanghai), south (Zhejiang) and west (Anhui) of the Yangtze River Delta region, but there are significant differences in the terms of long cycle scales. (4) The PM_2.5 concentration still shows obvious periodicity within 240 h during severe haze in the Yangtze River Delta region. This paper provides a framework for the government to make policies on energy conservation, emission reduction and air pollution control, and also provides a strong basis for haze prediction.

Natural disasters have been happening more frequently in the world in recent years. Different groups of economists have different opinions towards the economic impact after these natural disasters. One group believes that natural disasters will promote the economic growth in the area where natural disasters occurred and the other believes strongly that natural disasters will hinder economic growth. In this paper, we based on the provincial panel data between 2000 and 2010 in China including 31 provinces and categorized natural disasters into two different groups, i.e. meteorological and geological disasters. It has been found that meteorological disasters promote economic growth through the accumulation of physical capital, while the geological disasters have been found with no significant relationship economic growth of the local economy.

The economic loss evaluation of disaster is the basis for disaster prevention and disaster emergency management and thus needs to be solved urgently. Computable general equilibrium (CGE) model and input–output (IO) model are the major disaster assessment methods, which each have their own strengths and weaknesses currently. However, few comparative researches have been conducted on the economic losses of one disaster evaluated by CGE Model and IO Model based on the same disaster impact. Therefore, this paper, taking the Beijing “7.21 Rainstorm” in 2012 as an example, from the direct loss of agricultural and transportation sectors, used CGE Model and IO Model to calculate the comprehensive economic loss ratios of sectors and the final output loss of economic system, and then determined the range of the economic loss caused by the rainstorm. The results showed that: (1) CGE Model has a greater spillover effect upon sectors and a wider distribution of disaster-affected sectors than IO Model; (2) the range of economic loss determined by the assessment results of CGE Model and IO Model is [67.9396, 74.2739] (100 million yuan); (3) agricultural, transportation, and mining sectors are high-sensitivity sectors to rainstorm disasters. Through the comparison of the two models, the accurate economic loss of disaster can be obtained, according to which governments can take targeted measures for the prevention and early warning of disasters.

Tropical cyclones are one of the serious environmental disasters. However, researcher’s opinions are divided as to the impacts of tropical cyclones on labor employment. In order to investigate the general principle of tropical cyclones’ impact on employment, explore the reason of the divergence among existing research conclusions and put forward some suggestions for post-disaster reconstruction, this paper employed quantitative analysis of the literature—meta-regression analysis based on the existing literature. This paper studies the impact of tropical cyclones on the quantity of labor employed and employee remuneration from four aspects: industry dimension, time dimension, income dimension and tropical cyclone intensity, which clarifies the impact direction and intensity of the disaster in each dimension. The results show that: (1) Tropical cyclone disasters have greater impact on the employment of the primary industry, that is, the primary industry suffers the heaviest loss of employee remuneration. The impact of tropical cyclones on the employment quantity in the second and tertiary industries is greater, and the impact on the secondary industry is greater than the tertiary industry. (2) In the short term, the impact of tropical cyclones on employment is negative and the impact intensity is strong; in the medium and long term, the impact is positive and the intensity of impact is decreasing. Thus, through post-disaster restoration and reconstruction, the negative impact of tropical cyclones on the employment is gradually reduced. (3) Although Tropical cyclone disasters increased the quantity of labor employed from the low-income groups, it reduces their employment remuneration. In addition, the impact of disasters on the employment number of high-income groups is relatively small compared to that of low-income groups. (4) The higher the category of the tropical cyclone, the greater the positive impact on the employment of labor force. In the final part of the paper, the causes of these phenomena are analyzed, and suggestions are given on how to carry out post-disaster restoration and reconstruction activities. This paper is a useful supplement to the study of natural disasters’ impact on employment. The conclusions can provide reference for the emergency management of the disaster and the improvement of the labor market.

What impacts do typhoons have on local labor markets? Few empirical researches have been conducted in China. By collecting the data of 23 quarters of Guangdong province from 2009 to 2014 and using generalized difference-in-difference (abbreviated GDD), this paper analyzes the impacts of typhoons on labor markets from the perspectives of general effect, regional effect, intensity effect and time effect respectively. In addition, a comparative analysis is carried out between this study and similar studies of developed countries. The results show that: (1) though the effects of typhoons on employment are insignificant, high-strength typhoons can cause a 5.15% decline in per capita labor remuneration; (2) the effects of typhoons on labor remuneration, with cyclical characteristics, circle every four quarters. The negative impacts in the current quarter are followed by two quarters of positive adjustment and the final quarter of the recovery to pre-disaster levels; (3) the results of this study are different from those of the studies whose subjects are developed countries, which may be caused by the distinctiveness of China’s labor market. Finally, conclusions and corresponding suggestions are presented.

Resilience is a key term in disaster economic loss assessment. The flood loss rates in different scenarios for the residence, industry, commerce, infrastructure, and agriculture are calculated and the original depth-damage curves are drawn by collecting the socio-economic data of Lizhong Town in Lixia River and Houbai Town in Jurong River. Thereafter, by fully considering the resilience factor to draw the new depth-economic loss curves, which are in line with the actual situation, the results show: (1) the disaster reduction capability correction loss rate of different receptors in Lizhong Town is in the range of 16 to 74%, and in Houbai Town is in the range of 20 to 79%. The residential flood loss, commercial loss, infrastructure loss and industry loss were reduced by 74, 54.8, 38 and 16%, respectively in Lizhong Town, and the residential flood loss, commercial loss, and industry loss were reduced by 79, 60, and 20%, respectively in Houbai Town. (2) Residential property types and their display heights in families with different income levels have similar flood-damage rate curves in mainland China and Taiwan (Su et al. 2002); (3) The loss rate reached the maximum at the depth of 3 m, and the sum of the five receptors’ damage are 6.98 million Yuan (RMB) and 2.2 million Yuan (RMB) in Lizhong Town and Houbai Town, respectively. At last, in this paper, several approaches to improving resilience were proposed and possibilities in future researches were put forward. This study is a useful complement to flood economic loss evaluation literature and provides reference for disaster prevention and mitigation decisions in similar areas.

Due to the climate change and the rapid progress of urbanization, extreme weather disasters such as urban rainstorm and waterlogging are frequent. Therefore, how to find the waterlogging points in the presence of disasters and how to optimize the distribution of urban emergency logistics and reduce the negative impact of disasters have become a hot and difficult issue for government departments and scholars. First of all, the idea and method of using the big data of microblogging to obtain urban rainstorm and waterlogging disasters and public sentiment are put forward. In addition, this thesis constructed the location-routing problem (LRP) model of urban emergency logistics in the situation of rainstorm and waterlogging disaster, and found out the dynamic emergency distribution path of Nanjing in the situation of waterlogging disaster by using NSGA-III algorithm. Research shows that the risk management of urban rainstorm and waterlogging disasters, together with social media data, is a feasible way to obtain on-site data of disasters and carry out risk assessment of disasters. At the same time, the emergency logistics location-positioning model and algorithm can provide a reference for similar disaster emergency logistics distribution network and the conclusion can provide empirical reference for cities to cope with rainstorm and waterlogging disasters.

Background and Purpose: Increasingly frequent meteorological disasters have brought severe challenges that should be urgently handled in the sustainable development. However, meteorological data, loss data, social economic data and so forth relating to meteorological disasters rarely be effectively fused, failing to generate, rapidly and efficiently, economic losses and thus hindering the emergency management of disasters. Methods: A new economic losses evaluation information system has been developed for monitoring severe rainfall and flooding disasters in cities. The data mining method, econometric regression model and input–output model are implemented in the system, on the basis of multi-source data including hourly rainfall, geographical conditions, historical and real-time disaster information, socioeconomic data, and defense countermeasure. Results: Combined with the weather forecast information, this system can has the capability for reporting the real-time direct and indirect economic losses incurred by urban heavy rainfall and flooding disasters, automatically generating defense countermeasure reports for typical rainstorm and flooding points, and providing the spatial distribution of disasters. Conclusions: Finally, the system is conducive to improving the ability to manage disaster emergencies and eventually reducing the economic losses from the disaster.

Disaster insurance is an important tool for achieving sustainable development in modern agriculture. However, in China, the design of such insurance indexes is far from sufficient. In this paper, the single-season rice in Jiangsu Province of China is taken as an example to design the high-temperature damage index in summer and the low-temperature damage index in autumn to construct the formula calculating the weather output and single-season rice yield reduction. The daily highest, lowest and average temperatures between 1999 and 2015 are selected as main variables for the temperature disaster index to quantitatively analyze the relationship between the temperature index and the yield reduction rate of the single-season rice. The temperature disaster index can be put into the relevant model to obtain the yield reduction rate of the year and determine whether to pay the indemnity. Then, the burn analysis is used to determine the insurance premium rate for all cities in Jiangsu Province under four-level deductibles, and the insurance premium rate can be used for the risk division of the Province. The research provides some insights for the design of agricultural weather insurance products, and the empirical results provide a reference for the design of similar single-season rice temperature index insurance products.

Disasters are a serious challenge to human development and because climate change natural disasters are becoming more frequent. International aid, which can effectively alleviate resultant losses, has become an important way for humans to resist disasters cooperatively. Relevant research on how much each country should donate sustainably is barely seen. Against such a background, by taking into account indirect economic loss to and payment ability of donors, this paper propose the novel frame and detail input-output model for determining the amount of international aid that countries and multilateral organizations should allocate to disaster-stricken countries. To validate our method, the May 12th Wenchuan earthquake of 2008 is taken as example, and the indirect economic loss to each country is calculated based on the world input-output tables. Assuming that the total amount of aid is unchanged, the greater the indirect economic loss and payment ability are, the larger the amount of aid should be. Otherwise, the smaller the amount of aid should be. Based on the China’s Ministry of Foreign Affairs statistics, the results show that among the 41 donors, 6 countries including Indonesia, Australia, South Korea, Spain, Turkey, and Japan donated too much while 23 countries such as India donated too little. Next, the recommended amount of aid is given by considering the indirect economic loss to and payment ability of each country. The advantage of this framework and method lines in that the calculation results are objective and concise, while the disadvantage is that the indirect economic loss is relatively large and the recommend donate is uncertain. This paper demonstrates a new perspective for the study of standards for determining the amount of international aid that countries and multilateral organizations should provide.

Counterpart support (strategically coordinated disaster aid through partnership) policies provide guidance for emergency management and reconstruction, focusing upon short time period after natural disasters. However, determination of the quantitative levels of counterpart support remains a key challenge area in the literature, and practice. The goal of the research is to fill such gaps by introducing an index framework consisting of an inter-regional input-output model (IRIO) and a resilience index, where appropriate measures of indirect economic losses are developed from an IRIO, and the indirect economic losses are achieved by using the provincial economic resilience assessment index system. To examine the internal validity, including systematic error or bias, we investigated the reliability of the adopting models, calculation methods and index systems. To explore the external validity of the proposed measures and resilience index over the widely applied IRIO framework, data from the 2008 Wenchuan earthquake, an 8.0Ms earthquake that devastated parts of China, was utilized for obtaining parameter values of the framework. A follow-up investigation found that the fairness of counterpart support has been substantially enhanced and the satisfaction has been noticeably improved.

Clarifying the relationship between public cognition and satisfaction with meteorological service is an important way to adapt to and mitigate climate change. This paper first proposes an innovative concept on public meteorological cognition. Also, based on the survey data from 3,029 questionnaires on public cognition of meteorological disasters in Shenzhen city of China, the relationship among public cognition, perceived value, and meteorological service satisfaction is evaluated using a structural equation model (SEM). Research results demonstrate that: (1) Public cognition can significantly affect service satisfaction. (2) Shenzhen residents are generally satisfied with meteorological service, particularly during the typhoon season. However, the residents are dissatisfied with the availability of information on meteorological disaster warnings. (3) Both public meteorological cognition and perceived value of meteorological service significantly affect public satisfaction. (4) The public meteorological cognition can be improved by increasing the perceived value of meteorological service, which further enhances public satisfaction.

Concentrating on consuming coefficient, partition coefficient and Leontief inverse matrix, relevant concepts and algorithms are developed for estimating the impact of meteorological services including the associated (indirect, complete) economic effect. Subsequently, quantitative estimations are particularly obtained for the meteorological services in Jiangxi province by utilizing the input-output method. It is found that the economic effects are noticeably rescued by the preventive strategies developed from both the meteorological information and internal relevance (interdependency) in the industrial economic system. Another finding is that the ratio range of input in the complete economic effect on meteorological services is about 1:108.27–1:183.06, remarkably different from a previous estimation based on the Delphi method (1:30–1:51). Particularly, economic effects of meteorological services are higher for non-traditional users of manufacturing, wholesale and retail trades, services sector, tourism and culture and art, and lower for traditional users of agriculture, forestry, livestock, fishery and construction industries.

Under the background of climate change, the haze days in China have increased significantly, which seriously hinders the sustainable development of society and arouses wide attention from the public. However, the researches on the effect of air pollution on the stock yield of heavy pollution enterprise in key control cities are quite limited. Thus, this paper collects the AQI (air quality index) of key control cities (over prefecture-level) in China and the stock yields of listed heavy pollution enterprises in these cities from 2011 to 2016, and through multi-discontinuities regression model, testes the effect of air pollution on the stock yield of heavy pollution enterprise. The results show that: (1) severe air pollution (AQI = 300) has a significant negative influence on stock yield and the results are robust; (2) there is a time effect in the influence of air pollution on stock yield and the negative influence has become significant since 2013. This paper gives a brief discussion on the cause of it and suggests that severe air pollution should be strictly controlled. Only by facing air pollution seriously, can we eliminate air pollution with collective wisdom and concerted efforts and achieve the sustainable development of city. Being the first study to look into the effect of air pollution on stock yield in key control cities in China, this paper provides empirical reference for government supervision departments, stock investors as well as enterprises.

Haze pollution is a key obstacle for environmental management faced by China and many other developing countries. The survey on residential families’ economic losses and willingness to pay (WTP) are regarded as an essential reference for the implementation of environmental policies for haze treatment. For Jiangsu Province of China, authors of this paper first conducted three qualitative interviews with respectively meteorologists, meteorological administrators, and residents, a questionnaire was then elaborately designed, and subsequent surveys of 1,123 families were administered in Jiangsu province. Further, authors investigated measurements of direct economic losses by using the contingent valuation method (CVM), and explored influential factors of WTP by utilizing the binary logistic regression. From this survey, the estimated total economic loss incurred by haze disasters and total treatment cost for haze-related diseases were respectively 22.38 billions (in RMB) and 8.4 billions for Jiangsu Province. 55.9% of residential families were willing to pay 11.6 billions RMB annually (51.97% of total loss) for haze treatment, leaving a shortage of 11.05 billions RMB, which the government is responsible to pay. These findings provide empirical information reflecting the opinions of communities and residential families, useful for the governments and industrial sectors to design environmental policies to meet the requirements of the public and control environmental pollution in an effective way to achieve sustainable development.

To improve the air pollution of China fundamentally, effective measures should be proposed based on the thorough understanding of the characteristics of air pollution. Based on spatial econometrics, this paper investigates the characteristics and analyzes the determinants of the spatial concentration of PM_2.5 pollution in China. Results show that: (1) PM_2.5 pollution is highly concentrated in central and Eastern China, covering 17 regions which accounts for 75% of the total population and GDP (Gross Domestic Product); (2) The PM_2.5 values in China show a significant spatial correlation. Provinces such as Shandong, Henan, Anhui, and Hubei are high in PM_2.5 concentration. Meanwhile, these provinces are high in population density, GDP, and coal consumptions, and have a large amount of civilian cars. (3) PM_2.5 pollution shows spatial spillover effects. A 1% increase in the PM_2.5 values of neighboring provinces will lead to a 0.78% increase in that of one province. (4) An upward U-shaped relationship is observed between the density of per capita GDP and PM_2.5, and the PM_2.5 value is far from the turning point of growth. With the further growth of the density of per capita GDP, the PM_2.5 value is expected to increase rapidly and continuously. (5) Based on the characteristics of spatial concentration and spatial spillover, this paper proposes several prevention-control measures for haze pollution, such as stressing on the treatment of air pollution in severely polluted provinces, avoiding moving pollution industries to neighboring areas, performing joint prevention and control nationwide. Air pollution may only be rooted by transforming the pattern of economic growth.

As haze intensifies in China, controlling haze emission has become a top priority in the country’s environment protection endeavor. Since haze moves across different regions, it is necessary to develop a DEA (Data Envelopment Analysis) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co-opetition matrices of Chinese provinces, then builds the co-opetition DEA model that evaluates the haze emission efficiency of them, and finally uses the haze data for 2015 as an example to assess the applicability of the model. The results of the study include: First, compared with the traditional CCR model, this study constructs the co-opetition DEA cross-efficiency model that integrates haze’s feature of cross-border moving, and is thus more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained using the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision-making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, resulting from the evaluation based on the co-opetition DEA method, varies greatly: those with high efficiency are mostly inland provinces that have a slow-growing economy and adverse climatic conditions, while many of the provinces that have low efficiency are located in the relatively prosperous east China. The specific co-opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants that cross the border and can contribute empirical support to the haze reducing efforts of the government with its empirical results.

Currently, the hazy weather in China is increasingly serious. It is urgent for China to reduce haze emissions in environmental governance. A feasible way is to control haze emissions by optimizing the input sources. This paper proposed an innovative method in which the haze emission is controlled by readjusting input indicators. The output efficiency of input indicators in 29 provinces in China is calculated through 7 input indicators (namely, SO₂ emissions, NO_X emissions, soot emissions, coal consumption, car ownership, capital, and labor force) as well as GDP (desirable output) and PM_2.5 emissions (undesirable output). The results showed that, the input indicators are excessive in redundancy on condition that PM_2.5 emissions and GDP are equal. The input indicators are high in redundancy rate except labor force. The redundancy rates of soot emissions, SO₂ emissions and coal consumption are relatively high and respectively are 78%, 67.18%, and 61.14%. Moreover, all the provinces are redundant in inputs except Beijing, Tianjin, and Shanghai which are optimal in input–output efficiency. The redundancy of middle and western provinces, such as Ningxia, Guizhou, and Shanxi, is relatively large. The ideas and methods proposed in this paper can provide a reference for the future researches that aim to reduce the input indicators of undesirable output and the empirical results can provide empirical support for the PM_2.5 abatement in China.

It is an urgent task for China to control haze emission in environmental treatment. Emission right trading is a feasible way to achieve the reduction of pollutant emission on the premise that the total pollutant emission amount is under control, and the allocation of initial emission rights based on the target total amount is the key to emission right trading. In this paper, via the input-oriented ZSG-DEA model, the interprovincial allocative efficiency of PM_2.5 emission rights is investigated under the condition that the target total amount is fixed. The results showed that (1) after initial emission rights were allocated in accordance with the ZSG-DEA model, PM_2.5 emission amounts of all provinces would be in a new common DEA frontier so as to realize the overall Pareto optimality with a set total amount; (2) two factors, namely and areas and atmospheric environmental capacities of all provinces, were considered in the actual allocation to avoid the homogenization of all evaluated units found in the previous evaluation literature on allocative efficiency, thereby making the evaluation results more in line with the actual situations in all provinces. Such an investigation approach can provide guidance on the allocation of initial emission rights in emission right trading; and the research results can offer empirical support for haze-reducing work load conducted by central and local governments of China.

The emission of particulate matter (PM) and other atmospheric pollutants in port operations and shipping have caused a huge negative impact on the environment. Consequently, how to evaluate the environmental efficiency of ports and put forward corresponding countermeasures on this basis is becoming a more important issue than ever before from the perspectives of the government, academia, and society. We construct three data envelopment analysis (DEA) models to evaluate the environmental efficiency of ports under the circumstances of environmental control, non-environmental control and PM emission through inter-ports cooperation. The innovation of the DEA models constructed in this paper lies in: (1) Setting environmental control parameters to uniformly manipulate the situations of environmental control and non-environmental control, etc.; (2) Allowing non-equal proportional change of input index, expected output and non-expected output index; (3) Setting preference co-efficient for ports to admit favorable decisions; (4) Utilizing a distance formula of expected output under the situation of PM emission through inter-ports cooperation. Further, data from 11 major ports in China are collected to compare the expected output under different PM emission standards assuming the situation of environmental control and non-environmental control, port cooperation, and non-cooperative sewage discharge. The empirical results show that: (1) Ports in the eastern China (Shanghai, Ningbo, and Nanjing) have higher port efficiency; (2) Port cooperation can improve the overall expected output but it will lose its edge with the improvement of PM emission standards. (3) Ports follow the same trend of output loss regardless of favorable decisions. In the end, the author makes a summary, puts forward relevant policy suggestions and makes the recommendation for future research.

With the rapid economic growth and development, the problem of environmental pollution in China’s cities is becoming increasingly serious, and environmental pollution takes on a regional difference. There is, however, little comprehensive evaluation on the environmental performance and the regional difference of strictly-environmental-monitored cities in China. In this paper, the environmental performance of 109 strictly-environmental-monitored cities in China is evaluated in terms of natural performance, management performance and scale performance by Data Envelopment Analysis (DEA), incorporating PM_2.5 and PM₁₀ as undesirable outputs. The empirical results show that: (1) At present, the natural performance is quite high, while the management performance is noticeably low for most cities. (2) The gap between the level of economic development and environmental protection among cities in China is large, and the scale efficiency of big cities is better than that of smaller cities. The efficiency value of large-scale cities such as Beijing, Shanghai, Guangzhou, Shenzhen etc. is high, equaling 1; the value of smaller cities such as Sanmenxia, Baoding, Mudanjiang, Pingdingshan is low, close to 0, indicating that big cities are characterized by high environmental efficiency. (3) From the perspective of region, the level of environmental performance in China is badly uneven. For example, the environmental efficiency level of the Pan-Pearl River Delta region is superior to that of the Pan-Yangtze River region and the Bahia Rim region, whose values of environmental efficiency are 0.858, 0.658 and 0.622 respectively. The average efficiency of the Southern Coastal Economic Zone, Eastern Coastal Comprehensive Economic Zone, and the Comprehensive Economic Zone in the middle reaches of the Yangtze River is higher than that of other regions. Finally, the corresponding countermeasures and suggestions are put forward. The method used in this paper is applicable to the performance evaluation of cities, and the results of the evaluation reflect the differences of the environmental performance level between strictly-environmental-monitored cities and different regions in China, providing reference for the balanced environmental development of cities and regions.

Manufacturing industry is an important part of the national industrial system, and usually an industry with high carbon content. However, few studies have been carried out on the total amount, structure and the trend of the embodied carbon emission in the international trade of the Chinese manufacturing industry. Based on the input–output method, the thesis proposes the coefficient of direct carbon emission and complete carbon emission and a method for calculating the embodied carbon of the export trade. It also calculates the coefficient of direct carbon emission and complete carbon emission for Chinese manufacturing sector from 2000 to 2015 and breaks down the embodied carbon change of export trade in the manufacturing industry to technological effect, structural effect and scale effect by using the method of structural decomposition. Several inspiring conclusions could be drawn from the thesis. For example: (1) The coefficient of both the direct carbon emission and the complete carbon emission has been decreasing significantly, indicating the achievements of the energy saving and emission reduction of the Chinese manufacturing industry. (2) The embodied carbon emission from the manufacturing exports remains high and presents a rising tendency. The main sectors that exports the embodied carbon includes “S10 mechanical equipment and instrument”, “S9 metal products”, “S6 chemical industry”, etc., which should be the key sectors on reducing embodied carbon in exports. (3) The driving force of the embodied carbon exports lies in the scale effect of the manufacturing industry, on which the technical effect of the industry has a significant negative effect. The structural effect should have a positive influence that takes on a rising tendency; generally, this effect is only two thirds the scale effect. Finally, the corresponding policy suggestions have been made.