Industrial Sectors In China Research Articles

Systemic Risk Contribution and Contagion of Industrial Sectors in China: From the Global Financial Crisis to the COVID-19 Pandemic

This paper investigates the risk contribution of 29 industrial sectors to the China stock market by using one-factor with Durante generator copulas (FDG) and component expected shortfall (CES) analyses. Risk contagion between the systemically most important sector and other sectors is examined using a copula-based ∆CoVaR approach. The data cover the 2008 global financial crisis and the beginning of the COVID-19 pandemic. The empirical results show that the banking sector contributed most to systemic risk before and during the global financial crisis. Nonbank finance became equally important in 2020, and the COVID-19 pandemic promoted the position of the computer and pharmaceuticals sectors. The spillover effect diminishes over time, but there remains risk contagion between sectors. The risk spillover trend is consistent with that of systemic risk.

A BOOTSTRAP ASSESSMENT OF THE SHADOW PRICES OF CO2 FOR THE INDUSTRIAL SECTOR IN CHINA’S KEY CITIES

This study constructs a modified assessment model based on the bootstrap method to estimate the CO2 shadow prices for 35 typical cities’ industrial sectors in China from 2005 to 2018. Based on data from China’s seven pilot regional carbon markets, we evaluate the current CO2 trading price distortions. The empirical results indicate that during the study period, the average CO2 shadow price for the target cities is 1915.86 yuan per tonne, decreasing to 1880.57 yuan per tonne when using the bootstrap method for bias correction. The overall trends of CO2 shadow prices in most key cities are increasing given the strengthening of environmental regulations following the 11th Five-Year Plan. In addition, compared with the CO2 shadow prices in this paper, carbon trading prices in pilot cities present significantly negative distortions, which may fail to reflect the real opportunity cost of carbon abatement. Based on the findings of this study, several policy recommendations are proposed.

Contagion effect of systemic risk among industry sectors in China’s stock market

This paper constructs a tail event driven network to investigate the interdependence of tail risks among industries in the Chinese stock market from 2014 to 2019, and identifies systemically important industries that have made significant contributions to risk contagion by systemic risk decomposition technique. The empirical results suggest strong linkages among industry sectors. The risk profiles of certain industries under close supply–demand relationships are positively correlated, whereas the financial industry, particularly banking, proves to be the principal risk diversifier in the network, with the household appliance, food and drink industries performing likewise an important role in risk diversification. Based on the TENQR model, further study on additional information provided by the industrial chain structure demonstrates that the upstream industry dominates the spread of risks under extreme market conditions. Our findings are of constructive significance to the anticipative introduction of corresponding policies by regulatory authorities, and are also instructive to the investors’ allocation of assets.

Dynamic Green Growth Assessment of China’s Industrial System with an Improved SBM Model and Global Malmquist Index

This study proposes a method for resource management and optimisation in the industrial sector of China. Differing from previous research on the green assessment of industrial systems focusing on “black box” evaluation, our approach contributes to the two-stage structure of an industrial system that consists of an industrial production process and a pollution treatment process. The corresponding network slack-based model (SBM) is proposed to analyse the performance of China’s provincial industry sector. Based on our network SBM, the global Malmquist index is built to analyse the total factor productivity changes of system and individual processes to evaluate the consistency of sustainable development where dynamic green growth assessment is realized. The results show that the whole system and its pollution treatment process performance are poor and disorganised, while the industrial production process maintains a stable ranking for the 30 regions in China. We find that the main cause of this phenomenon is the variable technical efficiency change in the 30 regions, which reflects the immaturity of the management of the pollution treatment process. System performance is also highly related to regionalism.

To Advance Industrial Green Technology via Environmental Governance—Evidence from China’s Industrial Sector

Industrial green technology progress is an effective way to realize high-quality economic development in China. The different competitive incentives of local governments make a difference in the intensity of environmental regulation between regions. The intensity of environmental regulation is different in different areas of the same industry, leading to the inter-regional transfer of pollution enterprises. The regional distribution of industries is different, which determines the different industries’ needs to coordinate different regions. Only when various industries realize the coordination of regional governance can they jointly promote the progress of industrial green technology. Based on data from 33 industrial sectors in China from 2001 to 2015 and considering the institutional evolution of governance synergy, this study comprehensively investigated the influence mechanism of local government environmental regulation on the industrial green technology progress using the mediating effect model. We found that environmental regulation promoted industrial technological progress through governance synergy and a low degree of inter-regional regulation coordination hindered the industries’ green technology progress. With the change in inter-regional governance synergy levels, we further discovered that the impact of environmental regulation on industrial green technology progress changed substantially. At a low level of governance synergy, environmental regulation inhibited industrial green technology progress. At a high level of governance synergy, environmental regulation promoted industrial green technology progress. While strengthening environmental regulation, we should promote inter-regional cooperation at the industry level. Only by collectively enforcing pollution regulations in industrial level can industrial green technology progress be promoted.

Driving Forces of Water Intensity in China’s Industrial Sector: A Global Meta-Frontier Production–Theoretical Decomposition Analysis

The shortage of water resources has prohibited the sustainable growth of China. Identifying the driving forces of water intensity is critical to initiating cost-effective policies and regulations to reduce water consumption across China. We develop a global meta-frontier production decomposition approach, which could simultaneously address the spatial and temporal heterogeneities, to decompose the water intensity of the industrial sector in China at various levels from 2011 to 2015. Results show that the industrial water intensity in all provinces except Shanxi has been declining over the sample period, with little potential for a further reduction. Second, at the national level, the potential water usage factor and the temporal catch-up effect of water usage technology are two significant contributors in reducing the industrial water intensity. Third, we find that some factors have mixed results at the regional and provincial levels, calling for customized policies in these aspects. Our approach provides a more precise decomposition and reveals more details in China’s variations of industrial water intensity, which has manifold implications for regional water management.

How does industrial policy affect manufacturing carbon emission? Evidence from Chinese provincial sub-sectoral data.

Assessing the environmental impact of industrial policy is of great importance to China's industrial green transformation and high-quality development, while this topic receives little attention in the literature. Based on the panel data of 29 manufacturing industrial sectors in China's 30 provinces from 2006 to 2015, using a model that includes the year, province, and industry fixed-effect, this paper fills the gap by exploring whether and how industrial policy affects carbon emission in Chinese manufacturing. The result shows that industrial policy significantly increases carbon emission in supported industries by 10.3%, which is achieved by increasing industrial energy consumption, relaxing government environmental regulation, and encouraging enterprises to invest more in fixed assets. Further, the effects of industrial policy on carbon emission are heterogeneous in many aspects. In the central and western regions, technology-intensive industries, industries with stronger government intervention, industries with higher exports, industries supported by both central and local governments, and during the 12th Five-Year Plan, the industrial policy will increase carbon emissions more. Our findings for the first time indicate the negative impact of industrial policies on carbon emission reduction in the Chinese manufacturing industry, which implies that traditional selective industrial policies in developing countries like China need a paradigm shift to achieve green development.

Impact of COVID-19 on transportation and logistics: a case of China

Recent research has shown that the COVID-19 pandemic has holistically affected the financial and industrial sectors of China, in a considerably adverse manner. This paper aims to examine the impact of the COVID-19 virus on the transportation and logistics sector in China. In this regard, the study opted for a quantitative method of research design, along with primary data collection. The explanatory variable here is the COVID-19 virus, whereas, the dependent variables are air freight, ocean freight, and land freight. The data analysis technique used is the Structural Equation Modelling (SEM) analysis, comprising of the Confirmatory Factor Analysis (CFA) and the path assessment method. A structured survey questionnaire was also used, and the survey questionnaire scale was set from strongly agree to disagree strongly. The findings revealed that the effect of COVID-19 on air freight is statistically negative and significant. Moreover, the impact of COVID-19 on land freight is statistically negative and significant, whereas the effect of COVID-19 is statistically insignificant in the context of ocean freight, particularly during the period of the COVID-19 pandemic. Therefore, keeping these results in consideration, policymakers can enhance their support, so as to increase the performance of the logistics and transportation sector in China.

Depreciation Rate by Industrial Sector and Profit after Tax in China

This study examines how asset values by industry sectors are affected by different depreciation methods. We theoretically show that estimating depreciation rate by the Perpetual Inventory Method (PIM) contains more information than the method by Depreciation Expense as Accounting Item (DEAI), which are equivalent under certain conditions. Using data of 37 industrial sectors in China from 2001 to 2016, we estimate depreciation rates by sectors using both PIM and DEAI methods, which enable us to estimate capital stock and capital efficiency by sectors. By the panel estimation, depreciation rates estimated by both PIM and DEAI methods significantly increase with the enterprise's profits after tax defined by that profits before tax minus tax. Our result is consistent with the prediction of the economic depreciation hypothesis, implying that tax shield of depreciation that raises corporate after-tax cash flows to the firm could improve corporate investments for replacement by scrapping the old equipment.

The Impact of COVID-19 on the Dependence of Chinese Stock Market

By calculating the mutual information of stock indexes of 10 primary industry sectors in China, this paper analyzes the dependence relationship among Chinese stock sectors during the COVID-19 and the dynamic evolution of the relationship by using the sliding window method. According to the actual situation of the development of COVID-19 in China, the samples were divided into three stages, namely, calm period, pandemic period, and post-pandemic period. The results show that the dependence relationship among Chinese stock sectors is significantly enhanced in the pandemic period, but it decreases in the post-pandemic period and the dependence structure is similar to that in the calm period. The industrials sector is most closely connected with other sectors in the pandemic period. The information technology sector and telecommunication services sector maintain strong dependence in the three periods and share little contact with other sectors. In the pandemic period, the dependence between the consumer staples sector and other sectors is significantly enhanced, and consumer staples sector and health care sector maintain a strong dependence. From the results of the sliding window, the Chinese stock market is sensitive to the impact of COVID-19, but the duration of the impact on the dependence among the stock sectors is not long.

The interaction effects of GVC involvement and domestic R&D on carbon emissions: evidence from China’s industrial sectors

ABSTRACT In the context of China’s integration into global value chains (GVCs) and construction of lower-carbon economy, this study examines the effects of two different types of GVC involvement (i.e. GVC participation and GVC position) and domestic research and development (R&D) on carbon emissions, simultaneously and interactively. Using the panel data of 17 industrial sectors in China from 2005 to 2015, which were identified by merging the Organisation for Economic Cooperation and Development (OECD) industries and China’s National Economy Industries, we estimate GVC participation and GVC position. We then test the interaction effects of GVC involvement and R&D on carbon emissions, applying the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and feasible generalised least squares (FGLS) method. The results reveal that both GVC involvement and R&D have negative effects on carbon emissions. Moreover, the complementary effect occurs between GVC participation and R&D, while the substitution effect occurs between GVC position and R&D.

Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China

The growing energy consumption and associated carbon emission of Bitcoin mining could potentially undermine global sustainable efforts. By investigating carbon emission flows of Bitcoin blockchain operation in China with a simulation-based Bitcoin blockchain carbon emission model, we find that without any policy interventions, the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emission correspondingly. Internationally, this emission output would exceed the total annualized greenhouse gas emission output of the Czech Republic and Qatar. Domestically, it ranks in the top 10 among 182 cities and 42 industrial sectors in China. In this work, we show that moving away from the current punitive carbon tax policy to a site regulation policy which induces changes in the energy consumption structure of the mining activities is more effective in limiting carbon emission of Bitcoin blockchain operation.

Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China.

Based on the extended STIRPAT model and panel data from 2005 to 2015 in 20 industrial sectors, this study investigates the influential factors of carbon intensity, including employee, industry added value, fixed-assets investment, coal consumption, and resource tax. Meanwhile, by expanding the spatial weight matrix and using the Spatial Durbin Model, we reveal the spatiotemporal characteristics of carbon intensity. The results indicate that Manufacturing of Oil Processing and Coking Processing (S7), Manufacturing of Non-metal Products (S10), Smelting and Rolling Process of Metal (S11), and Electricity, Gas, Water, Sewage Treatment, Waste and Remediation (S17) contribute most to carbon intensity in China. The carbon intensity of 20 industrial sectors presents a spatial agglomeration characteristic. Meanwhile, industry added value inhibits the carbon intensity; however, employee, coal consumption, and resource tax promote carbon intensity. Finally, coal consumption appears to have spillover effects, and the employee has an insignificant impact on the carbon intensity of industrial sectors.

Air pollution reduction and climate co-benefits in China’s industries

Air pollution reduction policies can simultaneously mitigate CO2 emissions in the industrial sector, but the extent of these co-benefits is understudied. We analyse the potential co-benefits for SO2, NOx, particulate matter (PM) and CO2 emission reduction in major industrial sectors in China. We construct and analyse a firm-level database covering nearly 80,000 observations and use scenario simulations to estimate the co-benefits. The findings show that substantial co-benefits could be achieved with three specific interventions. Energy intensity improvement can reduce SO2, NOx, PM and CO2 emissions for non-power sectors by 26–44%, 19–44%, 25–46% and 18–50%, respectively. Reductions from scale structure adjustment such as phasing out small firms and developing large ones can amount to 1–8%, 1–6%, 2–20% and 0.2–3%. Electrification can reduce emissions by 19–25%, 4–28%, 20–29% and 11–12% if the share of electricity generated from non-fossil fuel sources is 70%. Since firm heterogeneity is essential to realize the co-benefits and directly determines the magnitudes of these benefits, stricter and sensible environmental policies targeting industrial firms can accelerate China’s sustainable transformation.

The effect of directed technical change on carbon dioxide emissions: evidence from China’s industrial sector at the provincial level

Technical change has a pivotal role to play in low-carbon development. Recent research has offered different insights regarding the effect of technical change on CO2 emissions but ignored the bias of technical changes which lead to changes in CO2 emissions. To fill the gap, this paper uses the 2008 to 2015 provincial-level data on China’s 22 industrial sub-sectors to investigate both the effect of directed technical change on CO2 emissions and its heterogeneity. We find that the technical change in most industrial sectors in China was capital-biased, although a labor-biased trend was evident. Labor-biased technical change is conducive to CO2 reduction, while capital-biased technical change has the opposite effect. Moreover, this effect is different by developmental periods, industries, and regions. Therefore, we propose that the government promotes labor-biased technical change based on the differentiated characteristics.

A simultaneous investigation of the environmental Kuznets curve for the agricultural and industrial sectors in China

We investigate the relationship between China’s high growth and environmental pollution from the agricultural and industrial sectors by estimating the environmental Kuznets curve (EKC). We used panel data of Chinese cities at the prefecture level and above for the period between 2011 and 2015 and estimated the EKC models using the system generalized method of moments (GMM). We found that agricultural chemical oxygen demand (COD) and industrial wastewater depict N-shaped and reverse N-shaped curves. Further, an EKC for agricultural nitrogen balance has been established. We find that industrial sulfur dioxide (SO2) emissions and their emission intensity support the EKC hypothesis. Our research is then used to provide policymakers with guidance on mitigating environmental pollution by comparing and scrutinizing the characteristics of agricultural and industrial pollution in China.

Innovation-driven development strategy and research development investment: a case study of Chinese sport firms

PurposeThis study investigated the effectiveness and applicability of China’s innovation-driven policies on encouraging sports firms to invest in research and development (R&D) activities.Design/methodology/approachThrough a series of multiple linear regression models, this study examined the direct and interaction effects of innovation-driven polices and firm characteristics on R&D investment for sport firms listed on the New Third Board in China.FindingsResults showed that financing constraints and certification as a high-tech enterprise were not themselves significant predictors of R&D investment; instead, the number of R&D staff and a firm's total assets were identified as key internal factors predicting the level of a firm's R&D investment. Other effective policy tools for stimulating Chinese sport firms' R&D investments included pre-tax deductions of R&D expenses, government R&D subsidies and income tax relief for high-tech enterprises, although their effects were heterogeneous.Research limitations/implicationsThis study observes a new theoretical discovery that when the financing constraints do not limit R&D investment, innovation-driven strategies remain effective tools to stimulate the R&D investment of sports firms.Practical implicationsThe findings provided practical guidance for both government–industry policymakers and sport business managers to prioritize the identified areas of significance when promoting R&D.Originality/valueFirst, this study focused on sport firms, which constitute a quickly growing industrial sector in China. The findings offered important insights for the government as well as corporate management with regard to promoting new industries and new enterprises. Second, this paper analyzed the effects of three special innovation-driven policies on R&D investment and explored enterprise innovation development in more detail. Third, this paper discussed not only the effects of innovation-driven policies on R&D investment but also the heterogeneity of their effects. The related conclusions could help improve the development, implementation and assessment of innovation-driven policies.

Energy efficiency of the mining sector in China, what are the main influence factors?

The mining industry is the basic industrial sector in China. Perfecting the energy efficiency of the mining sector is extremely important for ensuring China's energy security, emission reduction, and promoting China's economic growth. Various factors influence the mining sector's energy efficiency. Based on the provincial-level panel data in China, this paper adopts a two-way fixed-effect model to explore the effect of industrial agglomeration on the total factor energy efficiency (TFEE) of the mining sector in China. Also, the threshold panel model is used to calculate the impact of industrial agglomeration in regions with different economic development levels on the energy efficiency of the mining industry. The results indicate that increasing industrial agglomeration improves the energy efficiency of the mining sector. Furthermore, with the increase in regional economic development, the impact of industrial agglomeration on energy efficiency is gradually decreased. The government could reduce the capacity, which eliminates redundant mining capacity in eastern China, and develop high-quality mining clusters in central and western China.

뉴 노멀 배경 아래 중국 산업부문 탄소배출과 경제성장의 관계 및 영향요인에 관한 연구

뉴 노멀 배경 아래, 중국은 녹색 개발 촉진과 세계 기후 변화에 효과적으로 대처한다는 목표를 달성하기 위해 산업 구조의 변혁과 고도화를 추진하고 있다. 경제발전에 영향을 미치는 요인을 분석하고 산업부문의 탄소배출량을 줄이는 것이 핵심이다. 이 논문은 뉴 노멀이론, 공공사회학 이론, LMDI 모델에 기초하여 아래와 같은 점을 발견하였다. 첫째, 중국의 산업탄소 배출과 경제성장은 장기적 취약 디커플링 상태에 있다. 둘째, 에너지 집약도와 경제 규모가 산업 디커플링의 주요 촉진 및 억제 요인. 셋째, 뉴 노멀 배경 아래 중국의 주요 산업 부문의 탄소 배출과 산업 경제 성장 사이의 복잡한 관계는 추가적인 조정이 필요하다.

Carbon emission efficiency of China’s industry sectors: From the perspective of embodied carbon emissions

Carbon emission efficiency directly determines the overall carbon emissions level. Previous studies mainly investigated carbon emission efficiency from the perspective of direct carbon emissions without considering carbon emissions associated with intermediate production and consumption, resulting in deviations in the measurement results of carbon emission efficiency. This study distinguishes the differences between direct carbon emissions and embodied carbon emissions, including intermediate production and consumption, and constructs a noncompetitive input-output model to measure the embodied carbon emissions from 28 industry sectors in China between 2005 and 2017. The super-efficiency slack-based measure model was used to analyze the carbon emission efficiency of 28 industry sectors in embodied carbon emissions and direct carbon emissions scenarios. The results are as follows. First, China’s embodied carbon emissions and direct carbon emissions are generally increasing, with large differences in the total amount, growth rate, and rate of change. Second, China’s overall efficiency of direct carbon emissions and embodied carbon emissions has improved. However, there was significant heterogeneity among various industry sectors. Third, in the 28 industry sectors, the change in embodied carbon emissions efficiency was generally gradual, while its average value was significantly lower than that of direct carbon emissions efficiency, indicating that indirect carbon emission efficiency is lower than direct carbon emissions efficiency. Fourth, the reduction potential of embodied carbon emissions for the 28 industry sectors was less than that of direct carbon emissions, although both have decreased, with a greater magnitude of decrease in the former. Finally, this paper proposes countermeasures to promote low-carbon development of China’s industries based on improving intermediate production technology and implementing dynamic supervision.