The impact of Japanese nuclear wastewater discharge into the sea on the global economy: Input-output model approach.

As the division of work within the world economic system becomes increasingly complex, the impact of disturbing events on the economic system is expanding. Recently, Japan proposed to discharge nuclear wastewater into the Pacific Ocean, which will cause damage to marine fisheries, thereby seriously affecting fisheries and other industries in Japan and other countries and regions around the world. Considering different scenarios of final and intermediate demand shifting, this paper uses the Inoperability Input-Output Model (IIM) and Multi-Region Input-Output Model (MRIO) to simulate the economic consequences of nuclear wastewater discharge in Japan and calculate the economic changes of each industry and country (region). The results show that: In the short term, when only the final demand for Japanese fishery products decreases. (1) The ten countries (regions) with significant economic losses are Japan, the United States, Chinese Taipei, Canada, Chile, South Africa, Mexico, Peru, the United Kingdom, and Ireland. (2) The ten countries (regions) with a significant increase in total output due to demand shift are China (People's Republic of), the Rest of the World, India, Indonesia, Viet Nam, the Philippines, Brazil, Myanmar, the Russian Federation, and Malaysia. (3) A ranking of changes in the total output of different industries. In the long term, when both intermediate and final demand for Japanese fishery products decrease. (4) The change in value added in Japan. (5) The change in value added of 67 countries (regions) worldwide. The ten countries (regions) with the most significant increase in value-added are the Russian Federation, China (People's Republic of), the Rest of the World, the United States, Indonesia, Australia, Norway, Korea, Viet Nam, and Myanmar. The ten countries (regions) with the most significant decrease in value-added are Japan, Chinese Taipei, Chile, South Africa, Peru, Thailand, Mexico, Cambodia, Costa Rica, and Morocco. Changes in value added of 45 industrial sectors worldwide.

The green halo effect of China's OFDI: evidence from countries along the Belt and Road.

The growth of China's OFDI (outward foreign direct investment) is a unique feature of the "Belt and Road" project. Does China's OFDI have a green halo effect on countries along the Belt and Road (B&R)? Is this green halo effect somehow asymmetrical? What is the underlying mechanism? This paper systematically examines how China's OFDI exerts its influence on green technology spillovers, based on 56 B&R countries' 2003-2019 panel data. This study makes three significant findings: Firstly, China's OFDI has positive asymmetric characteristics in promoting green technology spillovers to host countries mentioned, which have lower income levels and openness. Secondly, strict relative environmental regulation can act as a "pressure pool," significantly enhancing the "green halo effect"; Thirdly, China's OFDI can help host countries obtain more green technology spillovers through three channels: expanding host countries' economic scale, upgrading host countries' industrial structure, and suppressing host countries' use of non-renewable energy. These findings point the way for 56 host countries to better accessing green technology spillovers.

Assessing the Impacts of COVID-19 on the Industrial Sectors and Economy of China.

Since December 2019, the COVID-19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative-based approach of assessing the potential impact of the COVID-19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision-making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4-8% in the absence of COVID-19, GDP growth in 2020 would be -8.77 to -12.77% after the COVID-19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.

A new economic loss assessment system for urban severe rainfall and flooding disasters based on big data fusion.

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.

Economic losses and willingness to pay for haze: the data analysis based on 1123 residential families in Jiangsu province, China.

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, the 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 1123 families were administered in Jiangsu province. Further, the 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 billion (in RMB) and 8.4 billion for Jiangsu province. 55.9% of residential families were willing to pay 11.6 billion RMB annually (51.97% of total loss) for haze treatment, leaving a shortage of 11.05 billion 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.

Disaster probability, optimal government expenditure for disaster prevention and mitigation, and expected economic growth.

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 substitution (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.

Design of Temperature Insurance Index and Risk Zonation for Single-Season Rice in Response to High-Temperature and Low-Temperature Damage: A Case Study of Jiangsu Province, China.

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.

Assessment of health and economic effects by PM2.5 pollution in Beijing: a combined exposure-response and computable general equilibrium analysis.

Assessment of the health and economic impacts of PM2.5 pollution is of great importance for urban air pollution prevention and control. In this study, we evaluate the damage of PM2.5 pollution using Beijing as an example. First, we use exposure-response functions to estimate the adverse health effects due to PM2.5 pollution. Then, the corresponding labour loss and excess medical expenditure are computed as two conducting variables. Finally, different from the conventional valuation methods, this paper introduces the two conducting variables into the computable general equilibrium (CGE) model to assess the impacts on sectors and the whole economic system caused by PM2.5 pollution. The results show that, substantial health effects of the residents in Beijing from PM2.5 pollution occurred in 2013, including 20,043 premature deaths and about one million other related medical cases. Correspondingly, using the 2010 social accounting data, Beijing gross domestic product loss due to the health impact of PM2.5 pollution is estimated as 1286.97 (95% CI: 488.58-1936.33) million RMB. This demonstrates that PM2.5 pollution not only has adverse health effects, but also brings huge economic loss.

A comprehensive estimation of the economic effects of meteorological services based on the input-output method.

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 nontraditional 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.