Evaluation and Factor Analysis of Industrial Carbon Emission Efficiency Based on “Green-Technology Efficiency”—The Case of Yangtze River Basin, China

In the context of low-carbon development, effectively improving carbon emission efficiency is an inevitable requirement for achieving sustainable economic and social development. Based on panel data of 11 provinces and municipalities in the Yangtze River Basin (YRB), ranging from 2000 to 2019, this paper uses green-technology efficiency to measure industrial carbon emission efficiency via stochastic frontier analysis (SFA) incorporated with carbon productivity. This provides a comprehensive analytical framework for assessing the carbon emission efficiency, quantitatively measuring the reduction potential, and clarifying the incentive channels. The results are as follows: (1) The industrial carbon emission efficiency (ICEE) of YRB presents an increasing trend. Although differences in emission efficiency among provinces and municipalities are narrowing, their emission efficiency is still prominently imbalanced. (2) The potential for reducing industrial carbon emissions in this region shows an upward-to-downward trend. The decline in such potential of each province and municipality in recent years indicates that further reduction is becoming more difficult. (3) Effective means to improve ICEE are to improve the level of industrialization, promote technological innovation in industrial low-carbonization, and raise industrial productivity. Meanwhile, the significant spatial spillover effect of ICEE further emphasizes the necessity of strengthening the coordination of carbon reduction policies in YRB. The research in this paper adds a new perspective to the evaluation of ICEE and also provides reference and technical support for the government to enhance ICEE and formulate green and sustainable development policies.

Key Theoretical, Policy, and Implementation Experience Considerations for the Mexican ETS: Toward an Equitable and Cost-Effective Compliance Phase

This chapter presents a brief overview of the policy design and theoretical environmental economic principles that underpin the concept of emissions trading systems (ETS) as a policy approach to address climate change. It discusses basic environmental economic principles pertinent to the development of market-based solutions to mitigate greenhouse gas (GHG) and co-pollutants. The chapter serves as the technical basis for the broader discussion that this book as a whole presents on the launch of the pilot phase of the Mexican ETS on January 1, 2020. Understanding international program design experiences, theoretical principles, and implementing best practices is key to ensuring Mexico’s success in the transition from the pilot or learning phase to an operational ETS compliance system. This will ensure Mexico fulfills its national climate policy goals and nationally determined contributions (NDC) under the Paris Agreement in a cost-effective manner, while also providing compliance flexibility to the industrial sectors covered under the program. A well-designed ETS ultimately provides the right incentives for industrial carbon emission reductions to drive cost-effective abatement and clean innovation. Secondly, this chapter presents a more in-depth review of policy developments focusing specifically on key implementation lessons from the two most advanced ETS systems in operation to date: (1) the European Union ETS and (2) California’s cap-and-trade program. In short, this chapter outlines a set of key policy lessons and design parameters to support the transition from the pilot Mexican ETS to an operational compliance phase in a socially just, environmentally sound, and cost-effective manner.

The UK Industrial Decarbonisation Strategy Revisited

In the period since 2010 successive UK Governments have produced various decarbonisation strategies for industry. This article scrutinises the most recent version that was published in March 2021: the Industrial Decarbonisation Strategy (IDS). It contrasts the policy content of the IDS with previous industrial roadmaps, action plans and strategies (including the Clean Growth Strategy of 2017). In addition, it compares the proposals in the IDS with the latest recommendations of the UK Government's independent Climate Change Committee, as well as drawing on lessons learned from the techno-economic assessments published by the author and his collaborators for a number of key ‘Foundation Industries’. The latter emit significant shares of UK industrial carbon dioxide (CO2) emissions: the iron and steel (∼25%), chemicals (∼19%), cement (∼8%), pulp and paper (∼6%), and glass (∼3%) sectors. They also produce some 28 million tonnes of materials per year, which are worth £52 billion to the UK economy, and account for ∼10% of UK total CO2 emissions.

Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

Accurate and scientific forecasting of carbon dioxide emissions will help make better industrial carbon emission planning so as to promote low-carbon industrial development and achieve sustainable economic growth. For depressing the disturbance of various elements, grey system-based models play an important role in forecasting science. In this paper, we extend the cumulative order from integer order to fractional order based on the discrete gray model, which we call CFDGM (1,1). After introducing the free quantity of the model order, the accuracy of the prevenient grey-based models can be further enhanced. We selected the data for carbon dioxide production by Germany, Japan, and Thailand for modeling. To obtain the optimal order of our grey model, we selected four optimizers to search for the order. The results show that although the search history of the four types of optimizers is different, the search results are the same, which proves that the four types of optimizers are stable and reliable, and the order for which we searched is reliable. By substituting the optimal order into CFDGM (1,1), we obtained the fitting and prediction error of the proposed model. The final results show that a satisfactory fitting effect and forecasting effect is obtained by our proposed model.

The Spatial Spillover Effect of Environmental Regulation and Technological Innovation on Industrial Carbon Productivity in China: A Two-Dimensional Structural Heterogeneity Analysis

Environmental regulation and technological innovation are two crucial factors for improving industrial carbon productivity. However, prior research ignored the spatial spillover effects of these factors, and heterogeneity caused by industrialization level and resource dependence did not acquire attention either. Thus, we use the STIRPAT model and spatial panel Durbin model to study the spatial spillover effects of two independent variables. Then, a two-dimensional structural heterogeneity analysis is conducted according to the industrialization level and resource dependence. The results are as follows: improving environmental regulation and technological innovation is good for industrial carbon productivity. Simultaneously, there are obvious regional differences under two-dimensional structural heterogeneity. From the perspective of space, industrial carbon productivity has high spatial autocorrelation, and it can be enhanced through local environmental legislation, as well as technological innovation. Environmental regulation’s spatial spillover impact inhibits the improvement of industrial carbon productivity in surrounding provinces, resulting in a pollution haven effect. However, there is no evident regional spillover effect of technological innovation. Therefore, we provided new perspectives from spatial spillover and structural heterogeneity to optimize low-carbon policies.