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The ecological compensation (EC) mechanism is an innovative institutional arrangement which can effectively realize the coordinated development of social-economic growth and ecological protection. The current research on the evaluation of the implementation effect of EC mechanism mainly focuses on its environmental performance, how the EC mechanism guides the economically underdeveloped areas in the upper reaches of the basin to embark on the road of high-quality economic development, especially the mechanism of how EC triggers the optimization and upgrading of industrial structure, it is a topic worthy of in-depth discussion. This study takes China's first cross-provincial horizontal EC mechanism pilot (Xin'an River Basin EC) as the research object and regards it as a quasi-natural experiment. This paper selects the annual panel data of the 11 cities involved from 2009 to 2019 and builds a DID model to study the mechanism of EC affecting industrial structure adjustment systematically. The results show that the EC mechanism mainly promotes the adjustment of the industrial structure in the pilot area through two paths, this is achieved by triggering the transfer of backward or highly polluting industries and promoting the upgrading of the industrial structure. At the same time, technological innovation plays a part intermediary role in the impact of EC to promote the upgrading of industrial structure. Further analysis found that the larger the funds of compensation input, the higher the assessment intensity, the better the effects are. Finally, this study puts forward relevant policy recommendations to further release the potential of the horizontal EC for the reference of decision-makers. • We evaluated the mechanism of ecological compensation (EC) on industrial structure. • EC affects the adjustment of industrial structure through industrial transfer and industrial upgrading. • Technology innovation plays a vital role in the process of EC mechanism guiding industrial upgrading. • The compensation amount and standard play a key role in the effectiveness of EC. [ABSTRACT FROM AUTHOR]

Ore recovery and dilution in caving mines are strongly determined by the flow characteristics of broken rock, especially under inclined boundary conditions. A physical draw model was designed and used to investigate the gravity flow characterization of granular materials during longitudinal sublevel caving. The three-dimensional (3D) shapes of flow axis, isolated extraction zone (IEZ) and ore remnants under inclined walls were obtained using the discharged quantity method and 3Dmine software, which indicated that stope boundary conditions and orebody dip have an impact on the characteristics of gravity flow. The experimental results also showed that the gravity flow characteristics of 3D draw experiments under the inclined walls were significantly different from those of the two-dimensional (2D) draw experiments. The flow axis is a line segment and lies in the same plane and only the width of the IEZ can be displayed in 2D experiment. However, the flow axis is a space curve segment in 3D draw experiment. Since being cut off by the front and inclined upper walls simultaneously, the 3D IEZ is more complicated than the 2D IEZ and both the width and thickness of the draw body are displayed simultaneously in 3D state. The shape and location of ore remnants in 3D state are also different from that in 2D state. Therefore, the 3D IEZ is more consistent with the actual situation than 2D IEZ for sublevel caving stope. The 3D gravity flow presented may be used to determine the design of the extraction layout and improve ore recovery during longitudinal sublevel caving. [ABSTRACT FROM AUTHOR]

• Analysis of the TBM performance at the Yingsong Water Diversion Project (China). • A deep learning model were developed to predict the Field Penetration Index (FPI). • The predicted FPI were verified by the measured FPI in different rock conditions. This work explores the potential for predicting TBM performance using deep learning. It focuses on a 17.5-km-long tunnel excavated for the Yingsong Water Diversion Project in Northeastern China with its 728 days' continuous monitoring of mechanical data. The prediction uses the deep belief network (DBN) proposed by Hinton et al. (2006) ,on the penetration rate, cutter rotation speed, torque, and thrust force. Field Penetration Index (FPI) is introduced to quantify TBM performance in the field. The DBN algorithm trains on n th number of preceding elements and predicts the performance of the n + 1th element. Prior to the implementation of the DBN, a pilot test was performed to find the optimal values for the network structural parameters (number of input nodes, number of hidden layers, number of nodes in the hidden layers, and learning rate). Predictions on FPI s in all the three rock types were then proceeded with good agreement with the field measured data. The mean relative errors for the predicted measured FPI s are generally less than 0.15 and the correlation coefficients (R) can be higher than 0.78. The predicted and measured FPI values along the length of the tunnel graphically follow the same trends. These results confirm the usefulness of big data and the deep learning in predicting TBM performance. [ABSTRACT FROM AUTHOR]

Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand during peak period through rewards. In this study, an incentive-based DR program with modified deep learning and reinforcement learning is proposed. A modified deep learning model based on recurrent neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then, reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods. It can support the development of incentive-based DR programs under uncertain environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs while reducing the peak electricity demand. A short-term DR program was developed for peak electricity demand period, and the experimental results show that peak electricity demand can be reduced by 17%. This contributes to mitigating the supply-demand imbalance and enhancing power system security. • An IDR-MDLRL algorithm was proposed for incentive-based DR program. • The uncertainty of environment was overcome by an MDL forecasting method. • A short-term DR program was developed for peak electricity demand period. • The peak electricity demand can be reduced by 17%. [ABSTRACT FROM AUTHOR]

Background: Effective innate immunity activation could dramatically improve the anti-tumor efficacy and increase the beneficiary population of immunotherapy. However, the anti-tumor effect of unimodal immunotherapy is still not satisfactory. Methods: Herein, a novel relay-type innate immunity activation strategy based on photo-immunotherapy mediated by a water-soluble aggregation-induced emission luminogen, PEG 420 -TQ, with the assistant of toll-like receptor 7 (TLR-7) agonist, imiquimod (R837), was developed and constructed. Results: The strategy could promote tumor cells to undergo immunogenic cell death (ICD) induced by the well-designed PEG 420 -TQ@R837 (PTQ@R) nanoplatform under light irradiation, which in turn enhanced the infiltration of immune cells and the activation of innate immune cells to achieve the first innate immunity activation. The second innate immunity activation was subsequently achieved by drug delivery of R837 via apoptotic bodies (ApoBDs), further enhancing the anti-tumor activity of infiltrated immune cells. Conclusion: The strategy ultimately demonstrated robust innate immunity activation and achieved excellent performance against tumor growth and metastasis. The construction of the relay-type innate immunity activation strategy could provide a new idea for the application of immunotherapy in clinical trials., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

The stabilization of growth and preservation of employment are the primary objectives of the current new economic normal. Investigating whether the low-carbon transition can be an opportunity or a shock for employment expansion in green development requires thorough examination. This study utilizes multi-temporal difference-in-difference (DID) models, analyzing comprehensive panel data from China (2007-2019) to assess the impact of the Low-carbon City Pilot (LCCP) policy on employment at meso-regional and micro-firm levels. Empirical findings robustly reveal that LCCP significantly boosts employment, with average treatment effects of approximately 0.548% and 5.892% at regional and firm scales, respectively. Positive impacts vary based on ownership, location, industry type, and energy consumption within enterprises. Notably, state-owned enterprises, those in the eastern region, engaged in secondary industries, and with high energy consumption experience pronounced positive effects. Mechanism analysis further reveals that LCCP boosts employment via promoting government environmental subsidies and expanding enterprise investment scale to create more jobs. These findings provide policy recommendations for further promoting low-carbon transition and expanding employment to achieve the win-win goal of sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Achieving carbon neutrality has become a global consensus, and plenty of measures and policies have been proposed in various industries to obtain this ambitious goal. As an innovative and radical environmental policy tool, personal carbon trading (PCT) policy which aims to reduce carbon emissions from the private consumption sector has aroused the attention. For a new policy tool, public support and acceptance is critical to obtain policy legitimacy and policy implementation. To implement PCT policy smoothly, the current research aims to explore the antecedents of public acceptance. From the emotional reaction perspective, this research mainly evaluated how political trust, perceived uncertainty and associated emotions matter for public acceptance of PCT policy. Based on the survey data, this research revealed that political trust has a direct positive impact on public acceptance of PCT policy. Meanwhile, political trust also has an indirect impact on public acceptance of PCT policy via emotions. Specifically, political trust is positively associated with positive emotions and negatively associated with negative emotions. Positive emotions promote public to accept PCT policy, while negative emotions inhibit public to accept PCT policy. The negative emotions exert a more powerful impact on public acceptance than positive emotions. Additionally, we found that perceived uncertainty exerts a negative effect on public acceptance and negatively regulates the relationship between political trust and public acceptance. That is, perceived uncertainty has a significant substitution effect on political trust. According to the research findings, policy implications to improve public acceptance of PCT policy were explored., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

Environmental emergencies have become a severe challenge to human society due to their great harmfulness, which seriously restrict the sustainable development of human society. Previous studies mainly focused on the pollutant sources and consequences of environmental emergencies, yet lacking highlight of the antecedents of environmental emergencies. Based on a panel data of 31 provinces in China, this paper establishes a mediating effect model to empirically investigate how government environmental enforcement and corporate environmental responsibility consensus (CCER) influence environmental emergencies. The results show that there exists a significant positive correlation between the intensity of government environmental enforcement and CCER, while CCER has negative influence on the frequency of environmental emergencies. In addition, CCER plays a full mediating role in the relationship between government environmental enforcement and environmental emergencies. In short, this research clarified the relationship between government environmental enforcement, CCER, and environmental emergencies, verified the intermediary role of CCER. This paper uncovers another "black box" of the mechanism of environmental emergencies, makes up for the limitations of previous studies from a single perspective, expands the scope of research on environmental emergencies, and proposes corresponding policy recommendations., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Personal carbon trading (PCT) policy has been considered as an innovative and radical environmental policy tool to achieve carbon neutrality in private sector. For a new policy tool, resident acceptance is extremely vital and should be considered first and put in a vital position. The aim of this research is to understand resident acceptance of PCT policy and examine what drives resident acceptance and opposition of PCT policy. Based on a national survey in China, this research analyzed the level of resident acceptance toward PCT policy and its associated driving factors. Results delineated that residents are more likely to accept the piloting of PCT policy in other city and more positive toward the implementation of PCT policy in the next five years, but less likely to accept the piloting of PCT policy in their city and more negative toward the immediate implementation of PCT policy across the country. Furthermore, this research uncovered that residents from different regions and living areas and with different income level have different acceptable level to PCT policy. Additionally, this research found that resident acceptance of PCT policy is significantly affected by PCT knowledge, perceived benefit, perceived cost, perceived policy effectiveness and environmental awareness. However, compared with other factors, environmental awareness plays a limited role in improving resident acceptance of PCT policy. On the basis of research findings, measures to improve resident acceptance of PCT policy were discussed., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Treatment of triple-negative breast cancer (TNBC) faces great challenges due to high invasiveness and poor prognosis. Therefore, effective treatment methods are urgently needed to control primary tumors and suppress distant tumors. Herein, we employed glycated chitosan (GC), a polysaccharide macromolecular immunoadjuvant, to construct a self-assembly GC@ICG nanoparticle which is accessible to tumor cells for synergistic cancer treatment based on the combination of phototherapy and immunotherapy. In this strategy, the self-associated synthesis of spherical GC@ICG significantly improved the stability of ICG and endowed GC with Trojan Horses in tumor cells to enhance tumor immunogenicity. A bilateral 4T1 tumor-bearing mouse model was established to evaluate the therapeutic outcomes and specific host antitumor immune response. Finally, GC@ICG-based phototherapy can directly eliminate primary tumors and resist the progression of untreated distant tumors. In addition, compared to the treatment of L + GC + ICG, GC@ICG-based phototherapy was evidenced to suppress lung metastasis and enhance infiltration of CD8 + T cells in untreated distant tumors. Therefore, this design shows promise in addressing the challenges of the treatment of TNBC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wu, Wang, Liu, Liu and Zhou.)

Currently, coronavirus disease 2019 (COVID-19) is spreading globally, which poses great challenges to the whole world and human beings. The aim of this research is to understand the determinants and residents' willingness to pay (WTP) for purchasing masks against COVID-19 in China. On the basis of protection motivation theory and contingent value method, this research shows that most residents are willing to purchase masks against COVID-19. COVID-19 knowledge, perceived severity, perceived vulnerability, and response efficacy are positively and significantly associated with residents' WTP and the WTP value. However, self-efficacy is only significantly associated with residents' WTP while not with WTP value. Furthermore, compared with other residents, residents in Hubei province have a higher level of COVID-19 knowledge, perceived severity, perceived vulnerability, self-efficacy and response efficacy, and the WTP value is higher. The average value of residents' WTP value for purchasing masks against COVID-19 in Hubei province is ¥120.92 ($18.73) per month during the epidemic, while it is ¥100.16 ($15.50) for other residents. In addition, the effects of demographic factors such as age, gender, income, etc., on residents' WTP and WTP value have also been examined.

Air quality issues, especially haze pollution, have become an important aspect that threatens the sustainable development and health of human beings. Previous studies on the environmental effects of emissions trading system (ETS) mainly focused on carbon emission reduction, instead of focusing on the synergistic governance effect between carbon emission and PM 2.5 reduction. Based on the PSM-DID method and the World Development Index (WDI) database, this paper examines whether the EU ETS has a spillover effect on PM 2.5 damage reduction, and discusses the related impact mechanisms. The research results show that the EU ETS promotes the reduction of PM 2.5 damage, and in different phases of implementation, the impact of the EU ETS on the reduction of PM 2.5 damage has a dynamic effect. The robustness test results also show that the research conclusions of this paper are highly reliable. Finally, this paper gives relevant policy suggestions, which can encourage countries to achieve carbon emission reduction targets while helping to reduce PM 2.5 damage, and eventually achieve a win-win situation between economic growth and environmental improvement., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Electric vehicles (EVs) are recognized as one of the effective measures to realize sustainable transport. Therefore, many countries have issued related policy initiatives and measures to promote EVs. However, consumer's cognition and acceptance of EVs is not ideal, which affects the early diffusion speed of EVs on the market. This research attempts to explore how consumer's driving experience affects EVs adoption intention from the perspective of consumer's emotional response (satisfaction and trust) by using stimulus-organism-response framework. Based on the analysis of 692 sample data collected through questionnaire survey, we found that consumer's driving experience has a significant and positive impact on EVs adoption intention. Meanwhile, driving experience also significantly and positively affects consumer's satisfaction while has no significant effect on trust. Furthermore, satisfaction has a significantly positive effect on trust, and satisfaction and trust are both significantly and positively associated with EVs adoption intention. The results further uncovered that satisfaction not only plays a mediating role between driving experience and adoption intention but also plays a multiple mediating role between driving experience and adoption intention together with trust. On this basis, relevant recommendations have been made to promote the development of EVs from the perspective of government and enterprise.

Bicycle-sharing is regarded as a new mode of transportation with low-carbon and low-cost advantages, which could effectively alleviate traffic congestion. Understanding the factors and influence paths that affect users' willingness to adopt bicycle-sharing is of great importance. This study aims to investigate how users' environmental concern affects their willingness to adopt bicycle-sharing through the impacts of attitude, subjective norm, and perceived behavior control. The research model was constructed based on the theory of planned behavior model and validated by empirical data collected from 988 users in China utilizing structural equation modeling technique. The results show that users' environmental concern is positively and significantly related to their attitude (β = 0.593, p < 0.001), subjective norm (β = 0.358, p < 0.001), and perceived behavior control (β = 0.508, p < 0.001) toward adopting bicycle-sharing. In turn, users' attitude (β = 0.496, p < 0.001), subjective norm (β = 0.209, p < 0.001), and perceived behavior control (β = 0.206, p < 0.01) toward adopting bicycle-sharing all positively affect the intention to adopt bicycle-sharing. Based on these results, policy implications for improving the users' acceptance and usage rate of bicycle-sharing and suggestions for future research are discussed.

Waste sorting is essential to address the current predicament of waste management. Though it is important, insufficient attention has been paid to explore residents' waste sorting intention and behavior and understand its formation process. To narrow the research gap, this research built a theoretical research model by adding personal moral norms and waste sorting knowledge into the theory of planned behavior to explicate residents' waste sorting intention and behavior formation process. Meanwhile, given the discrepancy between waste sorting intention and actual behavior, this research also explored the effect of external conditions, such as incentive measures, on this discrepancy. Based on survey data from 397 Chinese residents, this research found that attitudes, subjective norms, perceived behavioral control, personal moral norms and waste sorting knowledge were directly and significantly related to residents' waste sorting intention. Waste sorting knowledge also had an indirect influence on residents' waste sorting intention through attitudes and perceived behavioral control. Additionally, this research corroborated the discrepancy between waste sorting intention and behavior, and suggested that the link between intention and behavior was contingent on incentive measures. Incentive measures strengthened the effect of intention on behavior. This research is useful for understanding residents' waste sorting intention and behavior and valuable for encouraging residents to sort waste in their daily lives., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Electric vehicles (EVs) are known to be effective in reducing dependence on fossil fuels, in turn reducing the emission of greenhouse gas and other pollutants. To promote EVs, considerable efforts and measures have been taken. However, the public's awareness and acceptance of EVs are still low, which has affected the diffusion of EVs in the market. Based on the stimulus-organism-response (SOR) framework, this research aims to explore how consumers' EV driving experience (S) affects their emotions and cognitions regarding EVs (O: perceived relative advantage, perceived ease of use, and perceived risk of EVs) and then activates their intentions to adopt EVs (R). Based on survey data from 692 respondents, the results suggested that consumers' EV driving experience has a significantly positive effect on perceived relative advantage and perceived ease of use, and then perceived relative advantage and perceived ease of use positively affect consumers' intentions to adopt EVs. The EV driving experience negatively affects perceived risk, and perceived risk is also negatively associated with EV adoption intentions. Furthermore, consumers' EV driving experience also has a significant direct effect on EV adoption intentions. Based on the research results, relevant recommendations and suggestions have been proposed to promote EVs from the government and enterprise perspectives.

The coordinated development of environmental pollution and the economy has become a global problem. Emission trading scheme (ETS) has become a significant environmental policy instrument, prior studies primarily concentrated on micro-level factors with specific industries; there lacks discussion on the effects of ETS on macro-level industrial structure such as industrial structure upgrading. In this paper, we first use the propensity score matching (PSM) to screen out the control group in which countries are matched with the members of the EU, and then utilize the difference-in-differences (DID) method, to examine the effects of ETS implementation on national industrial structure upgrading in the members of the EU. Empirical results show that the EU ETS may does not have a significant impact on industrial structure change, while the implementation of EU ETS has significantly promoted the upgrading of the country's industrial structure, and the policy effect of EU ETS on industrial structure upgrading gradually increases as time goes by and there is a dynamic effect. The conclusions of this paper can be used as reference for the development of emission reduction policies in large countries with unbalanced internal development.

In the past 20 years, the green economy has increasingly attracted the attention of governments and policy makers. However, most studies have only focused on the relationship between the green economy and innovation, and little attention has been given to the relationship between the different innovation stages and the green economy. This study draws on eco-innovation and institutional theories and proposes a model to empirically investigate the effects between the different innovation stages and green economy. Furthermore, it explores how these effects are mediated by knowledge spillover and moderated by absorptive capacity and environment regulations. We use data from the last 5 years (2012-2016) obtained from different China regions to empirically test the model. Results show that knowledge innovation, research and development (R&D) innovation, and product innovation have the same significant positive impact on knowledge spillovers. R&D and product innovation have a significant positive impact on green economy, whereas knowledge innovation has no significant effect on the development of the green economy. Knowledge spillover partially mediates the relationship between the innovation stage and the green economy. Meanwhile, absorptive capacity positively moderates the relationship between knowledge spillover and the green economy. However, environmental regulation negatively moderates the relationship between knowledge spillover and the green economy. In addition, corresponding measures are proposed based on the conclusions.