Dynamic Pollution Emission Prediction Method of a Combined Heat and Power System Based on the Hybrid CNN-LSTM Model and Attention Mechanism

The prediction of pollution emission from a combined heat and power (CHP) system is very important for the production regulation and emergency response of a power system. The composition and structure of the CHP equipment are complex, and the production process is cumbersome. The fuel chemical reaction of the pulverized coal in the boiler represents a highly nonlinear and strongly interrelated process that is strongly affected by external environmental factors, which causes a certain level of volatility and uncertainty. In this study, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network-long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.

Design of Combined Auction Model for Emission Rights of International Forestry Carbon Sequestration and Other Pollutants Based on SMRA

In this paper, the combined transactions for emission rights of international carbon sequestration and other pollutants in forestry have been taken as the research object, and the Simultaneous Multiple Round Auction (SMRA) theory has been used to design a new model for the current auction transactions. In this paper, the feasibility and application of the SMRA model of reach object are studied by the methods of simulation experiment, model analysis, and analogical analysis, and the promotion of this model is discussed. The results show that the new auction model designed in this paper fills in the blank of the combined auction of international forestry carbon sequestration and other pollutant emission rights. It successfully eliminates the winners’ curse and the losses of the sellers. Meanwhile, it provides a new way of resolving ecological deficits problems, achieving the ultimate goal of an overall reduction in carbon and pollution emission. Moreover, it’s beneficial in resolving the structural contradictions between ecological purification and pollutants discharge, hence maximizing the benefits for all the stakeholders. Finally, it is suggested that the SMRA should be adopted in the international trading of emission rights of international carbon sequestration and other pollutants to promote the emission reduction of greenhouse gases and pollutants.

Does firm's philanthropy lead to more missions: theory and evidence

PurposeInspired by the comparison of charity donations among candidates in rural elections, the authors linked the non-profit motives of charity to corporate pollution emissions. And on this basis, the authors aim to provide theoretical and empirical explanations for the relationship between corporate philanthropy and pollution. The authors find that the desire to pursue more pollution emissions stimulates the firm's philanthropy, which is similar to the public welfare donations in rural elections.Design/methodology/approachFirstly, the authors construct a game-theoretical framework consisting of an entrepreneur and a bureaucrat to study the environmental cost of corporate philanthropy through the impact on pollution emission by the firm. Secondly, the authors used various empirical methods, including hybrid OLS, IV-2SLS, PSM, etc., to empirically test the impact of a firm's philanthropy on corporate pollution emissions. Finally, the authors use the output and abatement input as intermediary variables and apply the intermediary effect model to test the impact mechanism between corporate philanthropy and corporate pollution emissions.FindingsTheoretical model finds that the firm invests more in philanthropy discharges more emissions when the theoretical model is in political equilibrium. Besides, empirical results show that corporate philanthropy will lead to more pollution emissions by reducing abatement input and increasing production. Finally, the heterogeneity test finds that compared with state-owned enterprises, the intention of non-state-owned enterprises' philanthropy for more pollution emission is more obvious. Moreover, the improvement of regional environmental regulation can significantly inhibit the realization of corporate philanthropy's poor motive.Practical implicationsThe results have obvious policy implications for China's future policy-making. Firstly, regulatory agencies should pay close attention to the charitable behaviors of firms with serious negative environmental externalities, and prevent them from replacing more pollution emissions with philanthropy. Besides, due to weak environmental supervision in rural areas, rural polluting enterprises will be more inclined to make charitable donations to the village collective to obtain more emission rights. Therefore, the government should strengthen environmental supervision in rural areas to prevent enterprises from wanton pollution.Originality/valueBy constructing a game-theoretical framework consisting of an entrepreneur and a bureaucrat, the authors expound on corporate philanthropy's pollution motivation and decision-making mechanism for the first time in theory. Besides, this paper finds that the desire to pursue more pollution emissions also stimulates the firm's philanthropy. This paper expands the literature on corporate charitable donation motivations.

Convergence Study of Water Pollution Emission Intensity in China: Evidence From Spatial Effects

One of the challenges that China currently faces is how to reduce the emissions of the water pollution. However, the study of water pollution convergence has certain policy significance for controlling the emissions of water pollution. This article firstly uses chemical oxygen demand（COD）and ammonia nitrogen（NH3-N）as indicators of water pollution. Due to the obvious spillover effect of water in space, this article adds spatial effect to the convergence model. Based on panel data of 30 provinces and cities from 2006 to 2017, this article uses a dynamic spatial Dubin model to analyze the convergence of water pollution emission intensity to address the endogenous problem in the model. The empirical results of this paper show that there is absolute β-convergence and conditional β-convergence in the intensity of water pollution emissions. The spatial autocorrelation test shows that there is a positive spatial autocorrelation of water pollution emissions, which means that the pollution emissions in neighboring areas will affect the emissions in the local area. The industrial structure has a certain promoting effect on the emission of water pollution, which means that adjusting the industrial structure and alleviating the structure of the secondary industry is the trend of future development. Economic growth can curb the emissions of water pollution. The influences of urbanization and foreign investment on the emissions of the two pollutants are inconsistent, and policies can be formulated according to local conditions in the future.

Assessing the Impact of the National Sustainable Development Planning of Resource-Based Cities Policy on Pollution Emission Intensity: Evidence from 270 Prefecture-Level Cities in China

The question of how to achieve the sustainable development of resource-based cities has been a major concern for the whole world. In response, the Chinese government has introduced the National Sustainable Development Planning of Resource-Based Cities Policy (SDPRP) to address sustainable development issues in resource-based cities. However, few studies have evaluated the environmental effects of the implementation of the SDPRP. Therefore, difference-in-differences (DID) and mediation effect models were applied to investigate the impact of the SDPRP on pollution emission intensity using balanced panel data for 270 prefecture-level cities in China from 2003 to 2018. The statistical results reveal that the SDPRP significantly reduced pollution emission intensity. Robustness test results showed that the conclusions are robust. Furthermore, the inhibitory effect of the SDPRP on pollution emission intensity increased year after year. We also found that the SDPRP can reduce pollution emission intensity by facilitating technological innovation, accelerating digital transformation, and improving human capital level, in which the role of human capital is stronger, while the role of digital transformation is weaker. The heterogeneity results suggest that compared with mature resource-based cities, the SDPRP had a stronger inhibitory effect on the pollution emission intensity in declining resource-based cities. However, the impact of the SDPRP on pollution emission intensities in growing resource-based cities was significant, while it was not significant in regenerative resource-based cities. Similarly, the SDPRP had a significantly greater inhibitory effect on pollution emission intensity in megacities than in large cities, while it increased the pollution emission intensity in small- and medium-sized cities.

The Environmental Effects of International Trade in China: Measuring the Mediating Effects of Technology Spillovers of Import Trade on Industrial Air Pollution

China is in a strategic phase of an industrial green transformation. Industrial air pollution is a key environmental target for governance. Because import trade is a core channel through which advanced environmental protection technology is absorbed, the question of whether technology spillovers brought about by import trade can reduce industrial air pollution emissions is a topic worth exploring. This paper uses a generalized spatial two-stage least-square (GS2SLS) model to explore the impact of import trade technology spillovers on industrial air pollution emission intensities using panel data from 30 provinces and cities between 2000 and 2017. Economic scale, industrial structure, and technological innovation are used as intermediary variables to test whether they play mediating effects. The results show that: (1) capital and intermediate goods technology spillovers directly reduce industrial air pollution emission intensity and (2) import trade technology spillovers indirectly reduce emission intensities by expanding economic scale, optimizing industrial structure, and enhancing technological innovation through mediating variables. Furthermore, industrial structure optimization and technological innovation have the largest mediating effects on industrial SO2, while economic expansion has the most significant mediating effect on industrial smoke and dust. The mediating effects of technology spillovers from intermediate goods exceed those of capital technology spillovers. Finally, industrial air pollution emission intensity demonstrates both spatial agglomeration and time lag effects. Environmental regulations and energy structure are shown to increase industrial air pollution emissions, while urbanization and foreign direct investment reduce industrial air pollution. Based upon these research results, some pertinent policy implications are proposed for China.