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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.

This paper aims to review how current policy instruments drive (or not) environmental innovation and, by doing so, to reinvestigate the relationship between innovation and regulation.

A comprehensive literature review on innovation and environmental regulation created a theoretical foundation of the paper. Using the grounded theory, a model was developed and evaluated using interviews. This is a timely topic as the new shape of recent environmental regulation appears to be fairly strict. A new model is presented to encapsulate highly dynamic interaction of environmental innovation and regulation to provide results that reflect on the present innovation behaviour and its implications.

The model highlights various diffusion pathways that are triggered by the main three drivers of innovation namely government (regulation), market (competition and cost) and technology which has the possibility of an autonomous diffusion.

The empirical data are limited to 13 qualitative experts' interviews within industry, consultancies and governmental departments.

The suggested model is particularly useful for policy makers to better understand the innovation dynamics and its diffusion pathways to design smarter regulations that incentivise rather than force organisations to comply with regulation.

The paper shows how regulation drives (or not) innovation and how various diffusion pathways can be used by external stakeholders to direct and promote innovation.

The purpose of this paper is to test the relationship between environmental regulations and green total factor productivity (GTFP) of China's logistics industry. The high-factor input, high-energy consumption, and high-pollution emissions model of the logistics industry developed within China faces challenges from severe resource and environmental constraints. It is generally believed that environmental regulations effectively restrain pollution emissions and help protect the environment.

The authors employ the undesirable slack-based Malmquist Luenberger model to calculate the GTFP across the provincial logistics industry and use the mediation effect model and threshold effect model to explore the effects and mechanics of environmental transmission regulations on the GTFP.

The main results show significant regional differences in the GTFP of logistics industry across China. In the transmission path of the impact of environmental regulations on the GTFP, regional innovation capabilities have mediation effects. Regional innovation capacities have a masking effect on the transmission path of environmental regulations on accumulated technical efficiency changes (AEC) and accumulated technical changes (ATC). The threshold effect test results show a dual-threshold effect between environmental regulations and the GTFP, with environmental regulations as threshold variable. Furthermore, the impact of regional innovation capability on the GTFP has a dual-threshold effect, with environmental regulation as threshold variable.

First, it is advisable to plan the environmental regulation policy system thoroughly and add supporting measures to ensure the efficiency and smooth implementation of the nation's environmental policies. Second, it is important to further understand the critical role of innovation capability in improving the GTFP. Third, there is an urgent need to standardize the operating behavior and market order of the leading players in the logistics market and to improve the operational efficiency of logistics enterprises.

So far, a systematical study researched on effect of environmental regulation on the GTFP in logistics industry was not published. This study can provide experience for the high-quality development of the logistics industry.

The purpose of this study is to analyze the effect of foreign direct investment (FDI) on pollution emissions and how environmental regulation affects this relationship.

In the empirical research, the authors selected panel data for 30 provinces in China from 2005 to 2019 as samples. First, the authors used the instrumental variable method to verify the existence of the above hypotheses in China. Then, the authors analyzed the moderating effect of different types of environmental regulations on the environmental effects of FDI. Next, in further discussion, the authors analyzed the difference between the environmental effect and the moderating effect in different time periods and regions, respectively. Finally, the authors discussed whether the different intensities of environmental regulations lead to the transfer effect of FDI in choosing investment destinations.

The result shows that FDI can help reduce pollution emissions and create a “pollution halo” effect, which is enhanced by command-and-control regulation but suppressed by market-based incentives. The heterogeneity analysis reveals that the 18th National Congress of the Communist Party has weakened the pollution halo effect of FDI, while the environmental effect of FDI in the eastern region is not significant, but in the middle and western regions, there is a significant pollution halo effect and a positive moderating effect of environmental regulations. Finally, further analysis reveals that FDI has a transfer effect under command-and-control environmental regulations.

First, the main purpose of this paper is to study the relationship between FDI and pollution emissions from the perspective of heterogeneous environmental regulation. Therefore, there is no detailed discussion on their effect mechanism of them. Second, limited by data, the authors adopt the single index to measure the stringency index of command-and-control and market-based incentive environmental regulations in China. The single index may not be able to fully reflect the intensity of regional environmental regulation, so the construction of a composite indicator is necessary. These shortcomings are the focus of the authors' future research.

Under the guidance of high-quality development, the conclusions above can provide reference for adjusting FDI policies and improving environmental regulation policies.

The innovations in this paper can be summarized as the following four dimensions: First, the authors use the instrumental variable (IV) method to address endogeneity in the relationship between FDI and pollution emission, which can further ensure the robustness of the research results and increases the credibility of the paper. Second, the authors distinguish between two types of environmental regulations to investigate their moderating effect on the environmental impact of FDI. Third, the authors consider the temporal and spatial heterogeneity of both the environmental effects of FDI and the moderating effect of regulation. Last, the authors analyze the spatial spillover of environmental regulation through the study of the transfer effect.

The aim of this article is to research on forestry green total factor productivity and explore the impact of financial support on forestry green total factor productivity.

The methods used in this study are super efficiency SBM model of undesired output and empirical model. SBM model is a kind of Data Envelopment Analysis (DEA). The SBM model with non-expected outputs (slacks-based measure) can be used to deal with the problem of efficiency measurement with multiple input and output variables and can be used to analyze the efficiency of green development of forestry economy.

First, the overall green total factor productivity of the authors’ country's forestry has shown a trend of first decline and then an increase from 2008 to 2018, and there are significant spatiotemporal differences; second, financial support has a significant positive impact on forestry green total factor productivity; third, environmental regulation has a significant threshold effect in the process of financial support on forestry green total factor productivity, and the role of financial support shows a trend of first increasing and then decreasing.

Secondly, taking the data of 30 provinces and cities in the authors’ country from 2008 to 2018 as the research object, using the super-efficiency SBM-Malmquist index to measure the country's forestry green total factor productivity and analyze its temporal and spatial changes; finally, a dynamic panel model was established to explore the impact of financial support on forestry green total factors quantitative impact on productivity, and adding environmental regulation as a threshold variable to establish a dynamic threshold regression, and found that financial support has a nonlinear impact on forestry green total factor productivity.

In the post‐war years of fast economic growth extensive government regulations to curb growing environmental problems were developed in most Western countries. This regulative activity was directed primarily at larger fixed industrial installations with locally identifiable environmental effects. Since then, however, smaller and mobile sources, which are more difficult to regulate effectively, have increased their share in pollution. Now the scale of environmental problems has also changed from being mainly a local one to being a regional, a national, an international and even a global one. Examples of this are the diffusion of durable toxic substances, eutrophication, acid rain, depletion of the higher ozone layers, and the rising temperature of the atmosphere. Moreover, perceived social costs of environmental policy and other types of regulation have risen, inspiring a worldwide movement towards deregulation and privatisation. So there is ample reason to reconsider the instruments of environmental policy used so far and to look for ways to amend environmental effectiveness and social costs. In section I some of the directions which the search for improved instruments might take are identified and in the subsequent sections certain financial, social and product‐oriented instruments are analysed in more detail.

The purpose of this paper is to explore the relationship between formal environmental regulation (FER) and informal environmental regulation (IER), technological innovation and employment.

This paper uses data from the 30 provinces of China during 2003–2015. The impacts of formal and IER and technological innovation on employment are estimated by generalized least squares, and the eastern region of China is analyzed separately.

First, both formal and IERs have different degrees of significant impact on employment, and the relationship is not a simple linear. FER has an inverted U-shaped relationship with employment, but IER has a U-shaped relationship. However, in the test including technological innovation, the results of the national sample and the eastern sample are different. In the eastern sample, the relationship between informal regulation and employment has an inverted U-shaped curve. Second, the results of model 3 and model 6 show that technological innovation has a significant negative effect on employment both in the national and the eastern region sample.

This paper puts forward corresponding policy implications: first, in designing environmental regulations, it is necessary to consider not only the stringency but also the type of regulation. Second, environmental regulations need to be differentiated by region. Finally, when designing environmental regulations, it is necessary to consider more flexible employment policies that are contingent on the stringency of regulations, in order to prevent employment decline due to technological innovations.

The conclusions about the influence of environmental regulation on employment reached are not consistent in China. Most existing research studies seldom consider environmental regulations into categories and focus only on the whole environmental regulation. This paper pays attention to the influences of different types of environmental regulations on employment. It analyzes the eastern region separately to explore whether there is a difference in the effects of environmental regulations. Furthermore, this considers the effect of technological innovation as a mediator.

Environmental regulation is in a continuous state of intense change and modification amid the long-term tensions between environmental protection and economic growth. In this article, the authors creatively investigate how fluctuations of environmental regulation influence a nation's economic growth while also examining the mediating effect of technological innovation.

Using sample data of 36 Organisation for Economic Co-operation and Development (OECD) countries from 2013 to 2018, environmental regulation is differentiated in two aspects of formal environmental regulation (FER) and informal environmental regulation (IER) and analyzed to assess the effects of regulatory fluctuations on investment and technological innovation.

The research results demonstrate that both FER fluctuation and IER fluctuation exert a significant negative impact on economic growth. These two fluctuations in environmental regulation increase uncertainty and unpredictable risks for corporations and investors, significantly stifling the willingness to contribute to innovation activities and leading to a diminished level of innovation. Technological innovation is revealed to have a mediating influence on the relationship of environmental regulation fluctuation to economic growth.

These findings enrich the research on the impact of environmental regulation from a dynamic, multinational perspective, contributing to the literature by exploring the relationships between environmental regulation fluctuation, technological innovation and economic growth at the OECD-country level.

This paper aims to explore the relationship between environmental regulation, technological innovation and manufacturing quality competitiveness to provide some references for emission reduction activities and improvements in manufacturing quality competitiveness to achieve environmental protection targets and economic development as part of a win–win situation.

Based on the structure-behavior-performance paradigm and Grabowski’s research, a new empirical model was provided. The software, EViews 6.0, was used for econometric analysis. Regression analysis was adopted to explore the three indicators’ relationships.

First, environmental regulation can promote technological innovation effectively. Second, compared with wasted gas and wasted solids, investment in wasted water control promotes Chinese technological innovation most. Third, the impact of research and development investment, induced by environmental regulation, on manufacturing quality competitiveness is greater than that induced by non-environmental regulation. Fourth, the impact of lagged two-phase environmental regulation on manufacturing quality competitiveness is similar to that of lagged one-phase regulation.

The issue that Chinese manufacturing is facing is how to manage the trade-off between pollution control investment and improved quality competitiveness. This study enables managers to understand how to better implement environmental regulation initiatives while achieving environmental protection and quality competitiveness as part of a win–win situation.

This paper analyzes the relationships between environmental regulation, technological innovation and manufacturing quality competitiveness for the first time and provides the basic argument for integrating Chinese environmental regulation with quality competitiveness to reveal the uniqueness of the circumstances determining China’s economic development.

The role of environmental regulations in inducing innovation and improving performance has been studied in the literature. However, there have been no studies in the UK using statistical data. This paper aims to study the links among regulations, innovation and performance in the UK using sector level data.

The paper used structural equation modelling to study the links among the three variables simultaneously.

The analysis indicates that environmental regulations in the UK are significant in improving economic performance of the industrial sectors. They also find that, in the short run, environmental regulations negatively influence innovation, and innovation negatively influences economic performance in these sectors.

The results have implications both for policy makers and firms in the UK industrial sector. For policy makers, environmental regulations have generally improved economic performance. For firms, the study shows that sufficient planning in meeting government's environment standards can help improve their economic performance.

This is the first study in the UK to explore simultaneously the links among the three variables: environmental regulations, innovation, and performance, using secondary sector level data.