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Known as the major capital providers in Belt and Road countries and the largest carbon emitter in the world, what role China's outward direct investment (ODI) plays in carbon neutralization has become a matter of concern. This study aims to measure the impact of China's ODI on the carbon emissions of Belt and Road countries.

Based on an econometric model and an inter-regional input–output model, a new model measuring the carbon emission effects of ODI is developed.

The empirical results show that (1) in general, China's ODI generates an emission-reduction effect in Belt and Road countries; (2) The relationship between the emission-reduction effect and income level of host countries shows an approximate inverted U-shaped trend; and (3) China's ODI generates stronger emission-reduction effects on capital-intensive industries.

This study quantitatively measures the scale of carbon emission-increase and reduction effect, which is relatively lacking in previous studies. This study explores the heterogeneity from the perspectives of regions, countries and industries. The authors have compiled an inter-regional input–output table for the Belt and Road countries for 2014 to provide a broad basis for the study of related issues.

The objective of this chapter is to discuss how different techniques in Regional Science and Peace Science and the emerging techniques in Management Science can be used in analysing Disaster Management and Global pandemic with special reference to developing countries. It is necessary for me to first discuss the subjects of Disaster Management, Regional Science, Peace Science and Management Science. The objective of this chapter is to emphasise that the studies of Disaster Management should be more integrated with socioeconomic and geographical factors. The greatest disaster facing the world is the possibility of war, particularly nuclear war, and the preparation of the means of destruction through military spending.

Tourism is a labor-intensive sector with extensive links to other industries and plays a vital role in creating employment. This study aims to propose a new framework to analyze the intrinsic structure of the employment effects of tourism-related sectors and their drivers.

This study uses input–output and structural decomposition analysis (IO-SDA) to quantify the employment effects of tourism-related sectors and their driving mechanisms based on China’s I-O tables of 2002, 2007, 2012 and 2017.

The results show a declining trend in the intensity of direct or indirect employment effects in tourism-related sectors, indicating a decreasing number of jobs directly or indirectly required to create a unit of tourism output. Among tourism-related sectors, catering has the highest intensity of indirect employment effects over the study period. Catering stimulates the indirect employment of agriculture, forestry, animal husbandry, fishery and food and tobacco manufacturing. The decomposition analysis reveals that final demand is the largest contributor to the increase in tourism employment, while technological progress shifts from an employment-creation effect in 2002–2012 to an employment-destruction effect in 2012–2017.

This study proposes a new analytical framework to investigate the structural proportional relationship between the direct and indirect employment effects of various tourism-related sectors and their dynamic changes. Doing so, it provides valuable references for policymakers to promote tourism employment.

旅游相关部门就业效应的驱动因素：以中国为例

摘要

旅游业是一个劳动密集型部门, 与其他国民经济部门有着广泛的联系, 这在创造就业方面发挥着重要作用。本研究旨在建立一个框架, 分析旅游相关部门就业效应的内在结构及其驱动因素。

本研究基于中国2002年、2007年、2012年和2017年的投入产出表, 引入投入产出和结构分解分析（IO-SDA）法量化了旅游相关行业的就业效应及其变化的驱动机制。

旅游相关部门的直接或间接就业强度呈下降趋势, 可见创造一个单位的旅游产出所需的直接或间接工作数量在减少。在旅游相关部门中, 餐饮部门在研究期内的间接就业效应强度最高, 主要带动了农、林、牧、渔业和食品及烟草制造业的间接就业。旅游就业效应变动的驱动因素中, 最终需求是旅游就业效应增加的最大贡献者, 技术效应从2002-2012年期间的就业创造效应转变为2012-2017年期间的就业破坏效应。

本研究建立了一个全新的分析框架, 可以揭示各个旅游相关部门的直接和间接就业效应之间的结构比例关系及其动态变化。对旅游就业效应的驱动因素分析可以为政策制定者提供针对性的建议, 以促进旅游就业。

Factores que impulsan los efectos del empleo en los sectores relacionados con el turismo: El caso de China continental

Resumen

El turismo es un sector intensivo en mano de obra con amplios vínculos con otras industrias y desempeña un papel vital en la creación de empleo. Este estudio propone un nuevo marco para analizar la estructura intrínseca de los efectos en el empleo de los sectores relacionados con el turismo y sus impulsores.

Este estudio utiliza el análisis de entrada-salida (input-output) y de descomposición estructural (structural decomposition) (IO-SDA) para cuantificar los efectos sobre el empleo en los sectores relacionados con el turismo y sus mecanismos impulsores, basándose en las tablas input-output de China de 2002, 2007, 2012 y 2017.

Los resultados muestran una tendencia a la baja en la intensidad de los efectos directos o indirectos del empleo en los sectores relacionados con el turismo, lo que indica un número cada vez menor de puestos de trabajo directos o indirectos necesarios para crear una unidad de producción turística. Entre los sectores relacionados con el turismo, la restauración tiene la mayor intensidad de efectos indirectos sobre el empleo durante el periodo de estudio. La restauración estimula el empleo indirecto de la agricultura, la silvicultura, la ganadería, la pesca y la fabricación de alimentos y tabaco. El análisis de descomposición revela que la demanda final es la que más contribuye al aumento del empleo turístico, mientras que el progreso tecnológico pasa de ser un efecto de creación de empleo en 2002-2012 a un efecto de destrucción de empleo en 2012-2017.

Este estudio propone un nuevo marco analítico para investigar la relación estructural proporcional entre los efectos directos e indirectos del empleo de varios sectores relacionados con el turismo y sus cambios dinámicos. De este modo, proporciona valiosas referencias para que los responsables políticos promuevan el empleo en el sector turístico.

This study aims to analyze the effectiveness and efficiency of research-based community service programs in Indonesia that used government funds in 2017–2021.

The design of this research is a quantitative research method using a data envelopment analysis to evaluate 370 leading universities in Indonesia. Furthermore, six analytical models were considered to compare effectiveness and efficiency between universities. It involved two resource (budget and staff academic involved), three output (intellectual property, prototype and publication) and three outcome variables (economic impact, social impact and capacity building).

The findings showed that several universities are considered necessary, with great potential to increase output and outcome efficiency in community involvement. The study mapped and divided the position of 370 universities for additional information. The effectiveness aspect provides another perspective in assessing the performance of tertiary institutions in Indonesia and can be an option for evaluating research performance to improve the quality of output.

The authors use data from research and community service management information systems used, both the resources used and the results. Efficiency and effectiveness of 370 universities were compared in this study, including comparing their position on the previous assessment with the assessment of the results of this study. Approach to the concept of Mandl et al. (2008) regarding the relationship between input, output and outcome as the main component of the indicators, the authors apply to analyze efficiency and effectiveness.

This paper aims to better understand the linkage between CO₂ emitters and industrial consumers. The border-crossing frequency is applied to calculate the average number of steps that a country takes in relation to the CO₂ emissions of its construction industry. The maximum border-crossing frequency and declining speed of CO₂ transfer are used to reveal the relationship between the length of production chains and the transfer efficiency of construction products.

This paper maps the CO₂ transfer that accompanies global production chains using the frequency of border crossing in the production processes of construction products. As the basic analysis framework, a multi-regional input–output model is adopted to analyse the average border-crossing frequency of CO₂ transfer. Additionally, indicators including the maximum border-crossing frequency and declining speed of CO₂ transfer are employed. Also, the maximum border-crossing frequency and declining speed of CO₂ transfer are used to reveal the relationship between the length of production chains and the transfer efficiency of construction products.

The results indicate that 85.49% of the CO₂ in construction products needs to be processed in at least one country, reflecting that direct trade is the major pattern of transfer of CO₂ from primary producers in global construction industries. The maximum border-crossing frequency is 4.88 for 15 economies, meaning that construction products cross the international borders up to 4.88 times before they are absorbed by the final users. The scale of the average border-crossing frequency ranged from 1.16 to 1.87 over 2000–2014, indicating that the original construction products crossed the international borders at least 1.16 times to satisfy the final demand of the consuming countries.

The data from the economic MRIO tables in the WIOD are only available until 2014, which is a limitation for conducting this research in recent years.

The fragmentation of production is not only reshaping global trade patterns, but also leading to the separation of CO₂ emitters and final consumers in production chains. A growing number of studies have focussed on the impact of production fragmentation on accounting for regional and national CO₂ emissions, but little research has been done at the scale of a specific industry. The major contribution of this paper lies in mapping the CO₂ emissions that accompany the production chains of construction products from the perspectives of both magnitude and length. Additionally, this paper is the first to propose using maximum border-crossing frequency and declining speed to analyse the characteristics of global production chains induced by the final demand of major economies for construction products.

Urbanization is driving the growth of China’s carbon footprint. It’s important to investigate what factors, how and to what extent, affect carbon footprints embedded in various categories of rural and urban households’ consumption.

We employ an environmental extended input-output model to assess and compare the rural-urban household carbon footprints and perform a multivariant regression analysis to identify the varying relationships of the determinants on rural and urban household carbon footprints based on the panel data of Chinese households from 2012 to 2018.

The results show evidence of urbanity density effect on direct carbon footprints and countervailing effect on indirect carbon footprints. The old dependency ratio has no significant effect on rural family emissions but has a significantly negative effect on urban direct and indirect carbon footprints. A higher child dependency ratio is associated with less rural household carbon emissions while the opposite is true for urban households. Taking advantage of recycled fuel saves direct carbon emissions and this green lifestyle benefits urban households more by saving more carbon emissions. There is a positive relationship between consumption structure ratio and direct carbon footprints while a negative relationship with indirect carbon footprints and this impact is less significant for urban households. The higher the price level of water, electricity and fuel, the lower the rural household’s direct carbon footprints. Private car ownership consistently augments household carbon footprints across rural and urban areas.

This paper provides comprehensive findings to understand the relationships between an array of determinants and China’s rural-urban carbon emissions, empowering China’s contribution to the global effort on climate mitigation.

Flood resilience is a critical concept in flood risk management (FRM). Meanwhile, flood resilience measurement has become vital for making the business case for investment in FRM. However, information is sparse on measuring the level of resilience of flood-prone communities in Nigeria. Therefore, this study aims to develop a fuzzy logic-based model for measuring the resilience of flood-prone communities towards achieving the United Nations Sustainable Development Goals (SDGs) 11 and 13.

This study describes the development of a fuzzy logic-based flood resilience measuring model, drawing on a synthesis of fuzzy logic literature and extant flood resilience. A generalisation of the flood system for a typical Nigerian community was made. It was followed by an identification and characterisation of the variables and parameters of the system based on SDGs 11 and 13. The generated data was transformed into a fuzzy inference system (FIS) using three input community flood resilience dimensions: natural, socio-technical and socio-economic factors (SEF). The model was then validated with primary data obtained from selected flood-prone communities in Ibadan, Southwest Nigeria. Expert opinions were used in rating the input dimensions for the selected communities.

In spite of various inputs from experts in the same study area (Apete, Ibadan, Nigeria), the resulting FIS generated consistent resilience indices for various natural, socio-technical and SEF. This approach can strengthen flood resilience measurement at the community level.

Although previous attempts have been made to measure flood resilience at the individual property level (Oladokun et al., 2017; Adebimpe et al., 2020), this research focuses on measuring flood resilience at the community level by adapting the fuzzy logic approach. The fuzzy logic-based model can be a tool for flood resilience measurement at the community level. It can also be developed further for regional and national level applications.

The European Union’s Digital Markets Act (DMA) calls for far-reaching changes to the way economic activity will occur in EU digital markets. Before its remedies are imposed, it is critical to assess their impacts on individual markets, the digital sector, and the overall European economy. The European Commission (EC) released an Impact Assessment in support of the DMA that purports to evaluate it using cost/benefit analysis.

An economic evaluation of the DMA should consider its full impacts on dynamic competition. The Impact Assessment neither assesses the DMA's impact on dynamic competition in the digital economy nor evaluates the impacts of specific DMA prohibitions and obligations. Instead, it considers benefits in general and largely ignores costs. We study its benefit assessments and find they are based on highly inappropriate methodologies and assumptions. A cost/benefit study using inappropriate methodologies and largely ignoring costs cannot provide a sound policy assessment.

Instead of promoting dynamic competition between platforms, the DMA will likely reinforce existing market structures, ossify market boundaries, and stunt European innovation. The DMA is likely to chill R&D by encouraging free riding on the investments of others, which discourages making those investments. Avoiding harm to innovation is critical because innovation delivers large, positive spillover benefits, driving increases in productivity, employment, wages, and prosperity.

The DMA prioritizes static over dynamic competition, with the potential to harm the European economy. Given this, the Impact Assessment does not demonstrate that the DMA will be beneficial overall, and its implementation must be carefully tailored to alleviate or lessen its potential to harm Europe’s economic performance.

The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change.

A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them.

The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements.

The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.