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Environmental regulation policy and cleaner production technology innovation are the key links to achieve sustainable economic development. This paper tests the impact of Two Control Zone (TCZ) environmental regulation policy on cleaner production technology innovation and explains the heterogeneity effect between them from the perspective of regional pollution intensity and R&D investment scale.

This paper takes TCZ policy as an environmental regulation policy and collects the patent data related to coal desulfurization cleaner production technology innovation in prefecture-level cities from 1994 to 2002 in China. This paper also tests the relationship between TCZ environmental regulation policy and cleaner production technology innovation based on difference in difference (DID) model. Take regional R&D investment scale and pollution intensity as category variables and analyze the heterogeneity effect of TCZ environmental regulation policy on cleaner production technology innovation based on difference-in-difference-in-difference (DDD) model.

TCZ environmental regulation policy effectively promotes China's cleaner production technology innovation, but it is more conducive to cleaner production technology innovation in heavy pollution areas. With the increasing of R&D investment scale, the positive effect of TCZ environmental regulation policy on cleaner production technology innovation is stronger.

On the basis of this study, the authors should further explore the regulatory factors of the relationship between TCZ environmental regulation policy and cleaner production technology innovation and further expand the research object, so as to make the research conclusions more practical and instructive.

This paper tests the impact of TCZ environmental regulation policy on cleaner production technology innovation based on the prefecture city level data and DID model, and it handles the endogenous problem caused by the missing variables and provides the accurate conclusions. Moreover, this paper examines the heterogeneity effect of TCZ environmental regulation policy on cleaner production innovation from regional R&D investment scale and pollution intensity two hands and expands the existing theoretical research.

The purpose of this paper is to examine the effect of biased technology on employment distribution and labor status in income distribution of China. It also testifies a threshold effect of the capital per labor and employment distribution on labor status from biased technology.

This paper presents a normalized supply-side system of three equations to measure the bias of technology in China. Linear and threshold regressions approaches are applied over cross-province panel data to investigate the influence which biased technology has on labor status under different capital per labor and employment distribution regimes.

This paper empirically shows that technology has been mostly capital-biased in China. The regression results indicate that capital-biased technology impairs labor income status and tend to modify employment distribution and labor income between industries. Furthermore, it reveals the threshold effect of capital per labor and employment distribution on the relationship between biased technology and labor status.

This paper extends the literature by explaining labor status from the perspective of biased technology and the effect of inter-industry employment distribution in China. It further explores the asymmetric effect of biased technology on labor productivity and income, which promotes inter-industry labor mobility and modifies employment distribution. This paper highlights the implications of this explanation for labor relations and human resource management.

This paper takes the seven overcapacity industries such as the textile industry, electricity and heat, steel, coal, automobile manufacturing, nonferrous metals and petrochemical industry as research objects and proposes a TOPSIS grey relational projection group decision method with mixed multiattributes, which is used for the ranking of the seven industries with overcapacity and provided relevant departments with a basis for decision-making.

First, an evaluation index system from four aspects is established. Secondly, the attributes of linguistic information are converted into two-dimensional interval numbers and triangular fuzzy numbers, and an evaluation matrix is constructed and normalized. This paper uses the AHP method to determine the subjective weights and uses the coefficient of variation method to determine the objective weights. Moreover, this paper sets up the optimization model with the largest comprehensive evaluation value to determine the combined weights. Finally, the TOPSIS grey relational projection method is proposed to calculate the closeness of grey relational projections and to rank them.

This paper analyzes the problem of overcapacity in seven industries with the TOPSIS grey relational projection method. The results show that the four industries of automobile manufacturing, textile, coal and petrochemical are all in serious overcapacity levels, while the three industries of steel, nonferrous metals and electric power are relatively in weak overcapacity level in the three years of 2016–2018. TOPSIS grey relational projection method ranks the overcapacity degree of the seven major overcapacity industries, making the relative overcapacity degree of each industry more clear and providing a reference for the government to formulate targeted policies and measures for each industry.

By using TOPSIS grey relational projection method to evaluate the overcapacity of the seven major overcapacity industries, on the one hand, it makes the relative overcapacity degree of each industry more clear, on the other hand, it can provides the basis for the government and decision-making departments. This helps them promote better the healthy and orderly economic development of the seven major industries and avoid resource waste caused by overcapacity.

This article solves the single evaluation method caused by the limited indicators in the past, combines TOPSIS and the grey relational projection method and applies it to the overcapacity evaluation of the industry, not only applies it to the evaluation of overcapacity for the first time but also involves novel problems and methods, which expands the scope of application of the model.

The purpose of this paper is to investigate the effects of technology progress on carbon intensity in China. Abatement of carbon emission has become one of the most important targets for the Chinese government. Numerous studies confirm that technology progress is the main factor responsible for reduction in CI. However, very few studies analyze the impacts of technology progress on CI for various regions. There is also inadequate knowledge on the transmission mechanisms of the impacts. These are the motivations for this research.

Given energy consumption and CO₂ emissions, an improved MLPI, which stands for the generalized technology progress related to energy and environment, is introduced and decomposed into technical and efficiency changes. Using a panel data of 30 provinces from 1997 to 2012, the authors construct different panel data models to investigate the effects of technology progress (and its decomposition elements) on CI.

Results show that technology progress is conducive for reducing CI, with the main factor being technical change. It also finds that the two components of technology progress have completely different effects in the three regions of China. Dynamic panel data models with threshold effects indicate that capital deepening enforces and weakens the negative effects of technical change and efficiency change on CI.

The above conclusions provide new evidences for policy-makers with respect to capital deepening, technical innovation and allocative efficiency enhancement with a view to achieving CI reduction.

Technical progress is an important technique within improving China’s comparative advantages, as new and renewable technologies will be beneficial for energy security. Productive technical progress and green technical innovation are necessary to improve working conditions and productivity of industries. Therefore, the purpose of this paper is to study technical progress in China under such harsh competitive circumstances, as well as types of technical progress that can be promoted, productive technical progress or green technology progress, and how technical progress will affect China’s competitive advantages.

The authors perform a multi-index multi-factor constitutive model based on a sample of 468 Chinese industries, and divide the industries into four categories.

The results indicate that there is a “U”-shape relationship between green technology progress and comparative advantages and an inverted “U”-shape relationship between the intensity of market competition and comparative advantages.

China has crossed the inflection point of the “U”-shaped curve. This, coupled with the slowing of economic growth, demonstrates the need for advocating green technology in China to decrease the pollutant discharge. Establishing Chinese national brands within overseas markets and earning a profit through the downstream of production chain enhance China’s international competitiveness.

One of the most original findings of this paper points out that China is faced with a situation in which exports are severely decreased and domestic environment pollution is increased. Vigorous promotion of green technology progress, improvement of the quality and the technical content of exported products, the establishment of national brand within the overseas market, as well as enhancement of China’s international competitiveness, is needed.

The purpose of this paper is to analyse aspects of technological change in rice agriculture, related to adoption of the Green Revolution (GR) in Indonesia. Rice production is selected in this study because it plays an important role in the development of Indonesian economy. Particular attention is paid to the use of agrochemicals that has potentials of contaminating the environment.

The paper uses an econometric model to investigate the impact of different technologies and policies related to rice production. Production function technology that enables non-neutrality of input use is the underlying concept of this paper. Types of land and transformation in policies related to rice production were accounted for to determine biased technological change. National-wide data were compiled from the Indonesian Bureau of Statistics.

The results show that rice agriculture underwent technological progress with biased technological change. The technological change was capital- and labour-saving, and agrochemical-augmenting. Production system in wetland led to technological change less capital- and labour-intensive, whilst the GR led to technological change more labour-saving and more agrochemical-augmenting.

This study only pays attention to environmentally detrimental inputs as a cause of externalities. This is a not full representation of real environmental consequences. In some studies on environmental degradation associated with intensive agricultural practices, however, there are other factors that can degrade the environment, such as soil erosion and soil compaction resulting from certain agricultural practices, and deforestation resulting from agricultural expansion. These are also important environmental impacts. The author expects that these factors are interesting and challenging subjects to be modelled in future research on sustainability of agricultural productivity growth, both theoretically and empirically.

Increase in use of agrochemicals was strongly GR linked. Moving from the GR towards more environmentally friendly policy was a wise step to reach sustainable rice production. After the GR, an act that removed pesticide subsidies and disseminated environmentally friendly technology, called integrated pest management was able to reduce the intensity of agrochemical use in rice agriculture. Further actions to support environmentally friendly policy could be the use of bio-agents such as bio-fertilisers and bio-pesticides. Enhancing farmers’ knowledge on the environmental issues and engaging farmers as a part of agro-ecosystem would synergise the actions.

This study uses the concept of biased technological change, estimated econometrically using national-level data. The production function used in this analysis enables non-neutrality of agrochemical use. When the result significantly shows the agrochemical-augmenting technological change, it is a convincing evidence, not just by accident, that the GR really led to environmental problem.

The purpose of this paper is to evaluate skilled labor demand determinants in Brazil, considering alternatives explanations: changes in relative wages, non-homothetic technology output growth and skill-biased technical change.

This study relies on a rich and unique matched employer-employee data set for manufacturing sector, from 1996 to 2003. The analysis considers a translog functional form labor demand system estimated using seemingly unrelated regression and instrumental variables to control for possible measurement errors and wages and output endogeneity.

The demand function estimates suggest that: labor demand underlying technology is non-homothetic, research and development investment is biased toward skilled workers, the non-homothetic technology is not skill biased so output changes contributed positively for skilled labor increase, relative wages played a significant role and international trade has little explanatory power explaining labor demand shifts.

This is the first paper that considers alternative explanations for the increase in the demand of skilled workers for manufacturing in Brazil simultaneously: changes in relative wages, output changes with non-homothetic technology, skill-biased technical change and, to a lesser extent, international trade. The study challenges current empirical evidence that considers trade and trade liberalization as the main factor explaining labor demand shifts.

To measure total factor productivity (TFP) growth in industrial manufacturing for 15 major Indian states for the period 1982‐1983 to 2000‐2001.

Uses non‐parametric linear programming methods; TFP growth is decomposed into efficiency and technological changes and also measures for the bias in technical change. The resulting information is used to examine whether the post‐reform period shows any improvement in productivity and efficiency in comparison with the pre‐reform period.

Findings of the present exercise indicate the improvement in TFP. The recent change in TFP is governed by the technical progress in contrast with similar gain caused by the improvement in technical efficiency in the pre‐reform regime. The technological progress in state manufacturing exhibited a capital‐using bias during the study period. Regional differences in TFP persist, although the magnitude of variation has declined in the post‐reform period. Moreover, it is also found that there is a tendency for convergence in terms of TFP growth rate among Indian states during the post‐reform years and only the states that were technically efficient at the beginning of the reforms remain innovative.

Decomposing state level data on manufacturing into technical change and efficiency change helps in identifying the directions biases in favour of labour or capital. Also, this analysis demonstrates the richness of linear programming technique that allows for an investigation of important research questions on the underlying processes that influence TFP growth.

This paper explores the pattern of technical change in the Korean economy from 1970 to 2013 and investigates its determinants. We use the Classical growth-distribution schedule to show that the labor-saving and capital-using pattern has predominated. For the rationale behind this Marx-biased technical change, we focus on the relationship between technical change and real wage growth via the evolution of labor and capital productivity, and verify the historical direction of technical change against the rise and fall of the working class. Furthermore, we find that the deviation during the post-crisis period from the long-run trend of Marx-biased technical change is not attributable to the vitality of new technological innovations, but rather the reflection of class dynamics over extracting productivity under weaker capital deepening. The results suggest that the recent deterioration of labor share and labor unions in Korea is closely associated with low incentive for technological progress, which contributes to prolonged stagnation.

This article studies the evolution of the wage differentials between graduate (skilled) and non-graduate (unskilled) workers in several European countries from the beginning of the 1990s to the beginning of this century. The starting point is that all European countries show a common increase in the relative supply of skilled workers but different evolution of wage differentials. Economics theory usually relates the evolution of wage differentials not only to relative supply but also to skill-biased technological progress. I complement this explanation providing a theoretical model of wage bargaining where wage differentials are determined also by labour market institutions. My empirical findings show that both technological progress and labour market institutions are important in the determination of wage differentials. As for the former, I find that differentials depend on the pace and intensity at which technological progress takes place. As for labour market institutions, their effect, though important, is not always straightforward. In fact, some aspects of institutions, like minimum wage and the duration of unemployment benefits, favour unskilled workers while other aspects, like bargaining power and replacement rates from unemployment benefits, may magnify the differences in outside options and actually increase wage differentials.