Impact Of Industrial Structure Research Articles

Study on Carbon Emission Reduction Efficiency and Influencing Factors in Various Provinces in China

This paper establishes the framework of influencing factors of carbon emission reduction efficiency from two aspects of driving factors and braking factors and makes theoretical analysis. The panel data model is used to construct the model of influencing factors of carbon emission reduction efficiency. Collecting relevant data from 30 regions in China from 2011 to 2016 and selecting reasonable indicators，the impact of industrial structure, carbon rights market and technological innovation on carbon emission reduction efficiency have been empirically analyzed. According to the analysis conclusion, further calculate the energy efficiency of each region, and then come to carbon dioxide emission reduction potential, energy-saving space and emission reduction space of different regions. The concept of carbon emission decoupling is introduced to calculate the decoupling index of four stages in China and analyze the change of decoupling degree. The results show that: (1) The industrial structure greatly affects the efficiency of carbon emission reduction. The increase of the proportion of the secondary industry will increase the rate of change of carbon emission and reduce the efficiency of carbon emission reduction. The establishment of carbon rights market and improvement of technological innovation ability can effectively improve the efficiency of carbon emission reduction; (2) China's regional energy efficiency is not balanced. There is still great potential for carbon reduction, energy saving and emission reduction; (3) The stage decoupling index of different regions is different, but the degree of decoupling is strengthened year by year. The space of emission reduction is gradually reduced, indicating that the overall trend of economic growth on the pressure of emission reduction eased.

Provincial-level useful work and influencing factors analysis in China from 1985 to 2016: Based on logarithmic mean divisia index decomposition and affinity propagation clustering

Different from conventional energy analyses which focus on primary and final stages of energy flows, useful work analysis can describe the last stage of energy transformations to end-uses. Besides, useful work analysis considers the quality of energy flows as well as efficiencies and provide a robust thermodynamic framework for analyzing the efficiency of energy use. As the world's largest energy consumer, China has less number of studies related to useful work analysis and mainly centered on national scale besides ignoring the impact of differences among different regions. Owing to this, this study developed an analysis on the temporal and spatial characteristics of useful work in China on provincial-level during 1985–2016, and combined logarithmic mean divisia index (LMDI) decomposition with affinity propagation clustering to systematically evaluate the spatial distribution of the driving factors of useful work, thereby proposing a targeted energy policy proposal for each cluster of provinces. It has been noted that the useful work of southeastern provinces has increased significantly since 1995, while western provinces had some improvements after 2005 due to the “Western Development Drive” policy. Economic output and energy intensity were the two most important factors in most of the provinces, the efficiency effect was always positive and incremental and the impact of industrial structure and energy structure were complicated. Furthermore, it could be noted that the formulation of China's energy policies should consider the features of regional useful work and their driving mechanism, and more importantly it should be adjusted in a timely manner in order to respond to different developmental stages.

How Do the Industrial Structure Optimization and Urbanization Development Affect Energy Consumption in Zhejiang Province of China?

In response to global climate change, China has voluntarily assumed responsibility and has pledged to reach its peak in carbon emissions by 2030. Industrial structure and urbanization have important impacts on energy consumption. This paper empirically analyzes the dynamic influence of industrial structure and urbanization on energy consumption in the Zhejiang Province of China by constructing a structural vector auto regressive model using impulse response function and variance decomposition. The results show a positive impact of urbanization on energy consumption, which increases and then gradually decreases, and an impact of industrial structure on energy consumption. The results also indicate that it will take a certain period of time for an increase in the proportion of tertiary industry to curb the growth of energy consumption. Urbanization has a greater impact on energy consumption than does industrial structure.

The Effect of Labor Mobility on the Urban-Rural Income Gap—Based on 272 Prefecture-Level City Panel Data in China

The current income gap situation between urban and rural areas is still one of the important problems on the road of China’s economic development; based on the panel data of 272 prefecture-level cities in China from 2003 to 2012 and the theory of labor mobility, this paper uses the quantile regression and OLS regression to study the relationship between labor mobility and urban-rural income gap. The empirical results show that: labor mobility can effectively narrow the income gap between urban and rural areas, and with the increase in the number of quantile points, this reduction effect increases. The impact of industrial structure and industrialization level has changed at different quantile points, but the investment in science and education, the scale of financial development, and the impact of social security have not changed.

Energy Efficiency and Its Driving Factors in China’s Three Economic Regions

Energy efficiency improvement is essential for China’s sustainable development of its social economy. Based on the provincial panel data of China’s three economic regions from 1990 to 2013, this research uses the data envelopment analysis (DEA) model to measure the total-factor energy efficiency, and the Tobit regression model to explore the driving factors of efficiency changes. Empirical results show: (1) Energy efficiency, energy consumption structure, and government fiscal scale are significantly positively correlated. (2) Industrial structure and per capita income level have negative correlation to energy efficiency; the impact of industrial structure on energy efficiency is relatively small. (3) The increase of carbon dioxide emissions will decrease the energy efficiency. Furthermore, with people becoming less conscious of energy conservation and emission reduction, energy efficiency will also decrease. (4) Specific energy policies will improve energy efficiency, and greater openness in coastal areas will also have the similar effect.

Carbon emission trends of manufacturing and influencing factors in Jilin Province, China

This paper constructed a carbon emission identity based on five factors: industrial activity, industrial structure, energy intensity, energy mix and carbon emission parameter, and analyzed manufacturing carbon emission trends in Jilin Province at subdivided industrial level through Log-Mean Divisia Index (LMDI) method. Results showed that manufacturing carbon emissions of Jilin Province increased 1.304 × 107 t by 66% between 2004 and 2010. However, 2012 was a remarkable year in which carbon emissions decreased compared with 2011, the first fall since 2004. Industrial activity was the most important factor for the increase of carbon emissions, while energy intensity had the greatest impact on inhibiting carbon emission growth. Despite the impact of industrial structure on carbon emissions fluctuated, its overall trend inhibited carbon emission growth. Further, influences of industrial structure became gradually stronger and surpassed energy intensity in the period 2009–2010. These results conclude that reducing energy intensity is still the main way for carbon emission reduction in Jilin Province, but industrial structure can not be ignored and it has great potential. Based on the analyses, the way of manufacturing industrial structure adjustment for Jilin Province is put forward.

Impact of industrial structure and soil exposure on the regional variations in pulmonary nontuberculous mycobacterial disease prevalence

Objective/backgroundThe prevalence of pulmonary nontuberculous mycobacterial (pNTM) disease, including Mycobacterium avium complex (MAC), varies widely according to geographic region. However, the factors that influence regional variations in pNTM disease prevalence remain unknown. This study was undertaken to examine whether environmental or occupational factors or host traits could influence regional variations in pNTM disease prevalence. MethodsWe collected laboratory data on pulmonary tuberculosis (pTB) and pNTM from two hospitals in the West Harima area of Japan and five hospitals in Kyoto City, Japan from 2012 to 2013. We estimated microbiological pNTM disease prevalence by multiplying all pTB cases in each area with the ratio of pNTM cases and pTB cases at the survey hospitals in each area. We administered a standardized questionnaire to 52 patients and 120 patients with pulmonary MAC (pMAC) disease at Ako City Hospital and Kyoto University Hospital, respectively. ResultsThe estimated prevalence of microbiological pNTM disease in the West Harima area (85.4/100,000 population-years) was significantly higher than that observed in Kyoto City (23.6/100,000 population-years; p<.001). According to multiple logistic regression analysis, in Ako City Hospital, primary (activities directly related to natural resources) and secondary industries (construction, mining, and manufacturing primary industry produce; odds ratio [OR]=4.79; 95% confidence interval [CI]=1.49−14.0; p=.007) and soil exposure (OR=13.6; 95% CI=4.94−45.26; p<.001) were associated with pMAC disease. ConclusionEnvironmental factors, both industrial structures associated with occupational dust and environmental soil exposure, could influence the regional variations in pNTM disease prevalence.

Is it possible for China to reduce its total CO2 emissions?

China's CO2 emissions have been the focus of attention for domestic and foreign scholars. However, very few articles have analysed whether and how a reduction of China's total CO2 emissions can be achieved. This is of great significance for meeting China's future CO2 emissions reduction targets. Based on input–output decomposition analysis model and dynamic programming approach, this paper analyses the factors affecting China's total carbon emissions and discuss whether and how it could be possible for China to reduce its total CO2 emissions. The results show that carbon intensity is a major driver for the reduction of China's CO2 emissions and that the impact of industry structure on the increment of China's CO2 emissions has changed from positive to negative in recent years. Under the premise of economic growth, carbon intensity decline and industrial structure adjustment should be coordinated. A reduction in the total amount of China's CO2 emissions is difficult to achieve in the short term, but an effective development mode can be selected with some policy implications suggested.

Impact of Industry Structure and Product Characteristics on the Structure of B2B Vertical Hubs

Business-to-business (B2B) vertical hubs are expected to play an important role in the digital economy and add value by reducing inefficiencies in industrial procurement and enabling efficient interfirm interactions and collaborations. The last few years have witnessed both a proliferation of business models underlying these hubs and the failure of many of these hubs. This study identifies key structural dimensions of vertical hubs and develops a conceptual framework to characterize vertical hubs. We draw from the industrial organization theory and the marketing literature to examine the relationship between the characteristics of the vertical hubs, those of the industries they serve, and the products transacted through the hubs. Based on data collected from 63 B2B vertical hubs, we test our hypotheses relating industry and product characteristics to the structure of vertical hubs. The results provide modest support to our theoretical proposition that the characteristics of vertical hubs are likely to be shaped by the industry and product characteristics. We interpret the results and discuss the implications of the study for research and practice.

Dynamic Externalities, Local Industrial Structure and Economic Development: Panel Data Evidence for Morocco

Bun M. J. G. and El Makhloufi A. (2007) Dynamic externalities, local industrial structure and economic development: panel data evidence for Morocco, Regional Studies 41, 823–837. The impact of industrial structure on local economic development is analysed for the case of Morocco. Using annual data for six urban areas and 18 industrial sectors indicators for specialization, diversity and competition of firms within a particular region are constructed for the years 1985–1995. The effects of these and other explanatory variables on local economic activity are estimated using a dynamic panel data model with both individual and time-specific effects. The estimation results suggest significant positive specialization and diversity effects and significant negative competition effects asserting the importance of industrial structure for local economic development. The empirical evidence is robust to the measure of economic activity used in the analysis, i.e. either employment or value added. A similar analysis is...

RETRACTED ARTICLE: Environmental Impact Assessment of Industrial Structure Change in a Rural Region of China

As the embodiment of human activities, the change of regional industrial structure is an essential driving factor of global environmental change. Consequently, the research on the change of regional industrial structure and associated effects on the environment is one of the key issues of researches on sustainable development, human-environment relationship, and regional response to global environment change. However, compared to the flourish of researches on environmental impact assessment of industrial departments, few studies have been conducted to assess the environmental impact of regional industrial structure. In this study, based on a synthetic analysis of environmental disturbances of different industrial departments, the environmental impact coefficient of industrial department associated with the index of environmental impact of industrial structure was constructed, so as to make a quantitative assessment of environmental impact of the change of regional industrial structure. And the results of the case study in Lijiang City, a rural region of China, have showed that there are two obvious changes of industrial structure in the study area from 1992 to 2003, associated with a continuous decreasing of the index of environmental impact of industrial structure, which indicated a positive environmental effects of the change of regional industrial structure.

Labor Productivity Growth and Industry Structure: The Impact of Industry Structure on Productivity Growth, Export Prices and Labor Compensation

In this paper, labor productivity growth and its impacts are studied at the industry level. The development of productivity is analyzed in 54 industries in 14 EU countries and in the US between 1979 and 2001. The conclusion of the study is that the industry structure that leads to fast productivity growth is connected to falling export prices. The relationship between labor productivity growth and labor compensation growth is relative weak and, therefore, the majority of the utility resulting from the productivity growth does not benefit the labor force.

The Impact of Industry Structure on Strategic Corporate Social Responsibility

The Impact of Industry Structure on Strategic Corporate Social Responsibility

The impact of industrial structure and the exchange-rate exposure of industry portfolio returns

The purpose of this paper is to demonstrate that industrial structure is an important determinant of the exchange-rate exposure of industry portfolio returns. A time series regression is conducted on the sample of industries by regressing the rate of change of a trade-weighted US dollar index on the industry portfolio return while controlling for the US market. The regression was conducted using monthly data over a 3-year period (1995–1997). The results indicate that industries that are classified as being globally competitive and those that primarily serve the consumer sector of the economy have significant levels of exposure. The paper also provides some evidence on market efficiency as it pertains to changes in the value of the dollar.

The Impact of Industry Structure and Penalty Policies on Incentives for Compliance and Regulatory Enforcement

This paper examines the optimal fine for violations of environmental regulations, taking into account financial constraints facing regulated firms and the hierarchical structure of regulatory enforcement. Contrary to the existing literature, which suggests that maximal fines are sub-optimal, we find that the optimal fine is either the maximum amount the firm can afford to pay or zero (i.e., no regulation). The impact of a change in industry structure on the optimal fine, firm compliance and regulatory resource strategies is considered. We identify conditions under which the equilibrium level of regulatory resources decreases with an increase in the number of firms in the industry.

The impact of industrial structure on changes in unemployment in GB travel to work areas 1989-92

The impact of industrial structure on changes in unemployment has been hard to establish conclusively. This article examines this issue, for GB travel to work areas, for the period from December 1989 to December 1992 and concentrates in particular on the potential role of public and semi public sector employment in dampening the impact of recession in areas where such employment is relatively important. It is concluded that industrial structure was of some importance in the changes in unemployment over this time. A high percentage of employment in public and semi-public agencies did mitigate the impact of recession; other important explanatory variables included dummies to discriminate between north, central and southern regions, and changes in house prices.

Nonlinearities in the Impact of Industry Structure: the Case of Concentration and Intra-industry Variability in Rates of Return

This paper addresses the issue of industry concentration and intra-industry variability in rates of return. An inverted U relationship is hypothesized and tested in which one observed low levels of variability both at high and low levels of concentration, in one case as a result of collusion and the other as a result of competition. In the process, the paper highlights the benefits associated with combining both industry and firm levels of analysis. Copyright 1996 by Oxford University Press.

Human resource management in Austria

Highlights current HRM practice and its potential future developments in Austria. Develops a framework for analysing Austrian HRM, focusing on the regulatory environment at plant and supra‐plant level, the impact of industry structure on HR practice, and finally on geo‐political and economic influences. Using empirical data from the study of the Cranfield Network on European HRM (CRANET‐E), analyses the characteristics of current practice in Austrian HRM. Identifies and assesses future developments, especially those triggered by Austria′s accession to the European Union and discusses their implications for Austrian HRM theory and practice.

Market and Industry Structure and Corporate Cost of Capital

The impact of the structural attributes of industries on the cost of capital to constituent firms is examined within the framework of the Capital Asset Pricing Model (CAPM). The relationship between industry structure and systematic risk which the CAPM posits as the sole determinant of security returns is investigated using regression methodology. The results show that industry characteristics such as capital intensity, capital to labour ratio and entry barriers such as advertising have a significant influence on systematic risk and, hence, on the cost of capital to firms. RECENT US studies (e.g. Sullivan [1982]) have investigated the relationship between market power and the stock market return on equity using the Capital Asset Pricing Model (CAPM). Within the CAPM framework, investor required return is a linear function of systematic risk. The impact of industry structure on expected return is, therefore, likely to be transmitted through investors' perceptions of the relationship between the structural variables and firm systematic risk. Some recent theoretical studies have attempted to model such a relationship. This paper reports an empirical examination of the predictions resulting from these and other related models for a sample of UK firms. Our results show that industry structural characteristics e.g. capital intensity have a significant influence on systematic risk and hence cost of capital of firms, although the predictions of the extant theoretical models are only partly confirmed. The next section identifies a number of microeconomic determinants of corporate systematic risk. Section III discusses the methodology employed in the study. Section IV describes the sampling procedure and explanatory variables. Results are discussed in Section V. The final section provides a summary and the conclusions.

Modeling Competitive Marketing Strategies: The Impact of Marketing-Mix Relationships and Industry Structure

This paper empirically analyzes the formulation of competitive marketing strategies consisting of product quality levels, promotional expenditures and prices. Using a simultaneous-equation model, we examine the use of prices and promotional spending as signals or indications of product quality, the impact of promotional spending on prices, and the impact of industry structure on the formulation of the complete marketing mix. The structural equations are developed using a theoretical model of optimal competitive marketing mix, and are estimated using business-level PIMS data, which consists largely of industrial, durable goods producers. For a cross-section of 1,100 of these businesses, three results of the estimation are especially interesting: • Price-cost margins are high for high quality products, suggesting that high prices indicate or signal high quality. • Promotional intensity is unrelated to product quality, suggesting that promotional effort provides no reliable signals of quality. Intensely promoted products earn low price-cost margins, however, indicating that promotional spending is associated with high price sensitivity. • Quality levels, promotional intensity, and price-cost margins vary significantly with competitive and market conditions such as the concentration of competitors and the stability of market shares, although these relationships are generally weaker than relationships between quality, promotion, and price-cost margins. Quality levels are high and margins low in markets with unstable shares or unconcentrated competitors.