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The persistent combustion of fossil fuels has resulted in a widespread greenhouse effect attributable to the continual elevation of carbon dioxide (CO 2) levels in the atmosphere. Recent research indicates that utilizing CO 2 as a pyrolysis gasification medium diminishes CO 2 emissions and concurrently augments the value of the resultant pyrolysis gasification products. This paper reviews recent advancements in the pyrolysis gasification of organic solid wastes under a CO 2 atmosphere. Meanwhile, the mechanisms of CO 2 influence in the pyrolysis and gasification processes were also discussed. In comparison to noble gases, CO 2 exhibits reactivity with char at≥710 °C, resulting in additional mass loss of the sample. In addition, CO 2 was able to increase the specific surface area and stability of biochar and reduce biooil toxicity by lowering the content of cyclic compounds in the biooil, while CO 2 was able to react with GPRs with some volatile products (e.g., light hydrocarbons) to increase biogas yield. Finally, CO 2 also prevents catalyst deactivation by reducing secondary coke formation. We also recommend directing future attention toward utilizing unpurified CO 2 in pyrolysis and gasification. This review aims to expand the utilization of CO 2 and advocate for applying pyrolysis gasification products. [Display omitted] • An overview of recent studies on pyrolysis gasification in CO 2 atmosphere. • The effect of CO 2 on the pyrolysis behavior as well as on the catalyst is illustrated. • The mechanism of the effect of CO 2 on pyrolysis products is described in detail. • In the future, CO 2 and waste heat from industrial flue gases should be utilized directly. [ABSTRACT FROM AUTHOR]

Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises. • A tripartite evolutionary game of low-carbon innovation system among enterprises, government and the public. • Dynamic subsidy and tax mechanisms drive the model to converge to the ideal state. • Increased government subsidies will promote low-carbon innovation, but it isn't the more the better. • The same degree of public supervision is more effective for governments than for enterprises. [ABSTRACT FROM AUTHOR]

The increasing volume of electronic waste (e-waste) poses significant challenges for efficient collection in China. Despite many measures were taken over the past two decades, the e-waste collection rate was still not high. To this end, the Chinese government issued a new policy, the collection target responsibility (CTR) policy. Under the CTR policy, however, it is essential for participants to know how to share the responsibility of collection and how much reasonable targets are set to ensure the efficiency of the collection models. Therefore, the purpose of this paper is to explore the determination of optimal collection targets and the corresponding performance from the perspective of responsibility sharing to support the successful implementation of the CTR. Firstly, the study focuses on participants including the government, manufacturers, and recyclers, and develops three CTR models, independent collection model, government cost-sharing model, and enterprise collaboration model. Secondly, collection target equations for each model are established by employing dynamic differential game analysis, and corresponding collection performances are derived. Thirdly, through practical case simulations, the evolution of collection performance is dynamically analyzed to determine reasonable collection targets for the three models, as 23.8%, 32.3%, and 34.4%, respectively. The findings highlight the effectiveness of CTR in improving e-waste collection targets and performance, with the highest levels attained when the collection responsibilities are shared by government cost-sharing and enterprise collaboration. This study provides theoretical support for setting reasonable collection targets under CTR, and assists decision-makers in developing targeted CTR implementation measures. • The CTR model frameworks involving the main participants are developed. • Three CTR models are constructed from the perspective of shared responsibility. • The collection target for each CTR model is analyzed and determined. • The performances of CTR models are analyzed and compared. • Differences in the CTR models from cost-sharing and enterprises collaboration are observed. [ABSTRACT FROM AUTHOR]

Red mud (RM) is a kind of strong alkaline solid waste produced from the aluminum industry, which contributes significantly to environmental pollution and can cause severe health issues.Currently, RM is widely recognized as a potential material for soil remediation because of its rich metal oxide content, such as Fe/Al oxides. However, there is no comprehensive description on the roles of RM in passivation remediation of contaminated soil in mining areas. This review summarizes the mechanisms of passivation of heavy metals (HMs) in contaminated soil by RM, including precipitation, adsorption and ion exchange. Besides the effects of adding RM on soil physicochemical properties, heavy metal forms and ecological environment are further elaborated. Moreover, using the co-hydrothermal carbonization of RM and biomass for enhancing the efficiency of contaminated soil remediation is proposed as the main prospective research. This paper provides technical references for the resource utilization of RM and the treatment of heavy metal-contaminated soil. [Display omitted] • The remediation mechanism of RM on HMs in soil was analyzed. • RM affects HMs by precipitation, adsorption and ion exchange. • RM promotes photosynthesis and facilitates plant growth. • The addition of RM increases microbial abundance and enzyme activity in the soil. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the derived structure and properties of Zeolitic Imidazolate Framework-8 (ZIF-8), and the effect of residual structural on the catalytic properties after loading with Titanium Dioxide (TiO 2). For this purpose, we ingeniously prepare C-ZIF-8@TiO 2 with a transition-state defect structure and apply it for efficiently degrading organic dye wastewater represented by Rhodamine B (Rh–B). Thanks to the transition-state defect structure loaded with TiO 2 and ZIF-8 self-derived Carbon (C) and Zinc Oxide (ZnO), the catalytic performance of C-ZIF-8@TiO 2 is superior to that of TiO 2 and normal TiO 2 /ZIF-8 composites, and it is effective in degrading a variety of antibiotics and dyes. The related characterization also shows good photovoltaic properties and long-term durability for C-ZIF-8@TiO 2. The mechanism on free radical action is elucidated and the possible degradation pathway for Rh–B is speculated. Therefore, C-ZIF-8@TiO 2 provides a new strategy for the degradation of organic pollutants in water bodies. • A multidimensional catalytic system was created due to self-derived C and ZnO. • The transition-state defect structure formed using ZIF-8 as a carrier. • C-ZIF-8@TiO 2 effectively degrades a variety of antibiotics and dyes. [ABSTRACT FROM AUTHOR]

Groundwater resources play an important role for irrigation, particularly in arid and semi-arid regions, where groundwater depletion poses a critical threat to agricultural production and associated local livelihoods. However, the relationship between groundwater use, farming, and poverty, particularly with regards to informal mechanisms of resources management, remains poorly understood. Here, we assess this relationship by developing a behavioural model of groundwater user groups, empirically grounded in the politically fragile context of Tunisia. The model integrates biophysical aquifer dynamics, institutional governance, and farmer decision-making, all of which are co-occurring under conditions of aquifer depletion and illicit groundwater extraction. The paper examines how community-level norms drive distributional outcomes of farmer behaviours and traces pathways of local system collapse — whether hydrogeological or financial. Through this model, we explore how varying levels of trust and leadership, ecological conditions, and agricultural strategies can delay or avoid collapse of the social-ecological system. Results indicate limits to collective action under path-dependent aquifer depletion, which ultimately leads to the hydrogeological collapse of groundwater user groups independent of social and institutional norms. Despite this inevitable hydrogeological collapse of user groups, the most common cause of water user group failure is bankruptcy, which is linked to the erosion of social norms regarding fee payment. Social and institutional norms, however, can serve to delay the financial collapse of user groups. In the politically fragile system of Tunisia, low levels of trust in government result in low social penalties for illicit water withdrawals. In the absence of alternative irrigation sources, this serves as a temporary buffer against income-poverty. These results highlight the need for polycentric coordination at the aquifer-level as well as income diversification beyond agriculture to sustain local livelihoods. • Bankruptcy is the most common cause of water user group failure. • The erosion of social norms accelerates the collapse of groundwater user groups. • Local leadership serves as a key driver of collective action. • Illicit groundwater withdrawals, while accelerating aquifer depletion, serve as key livelihood buffers. • Polycentric coordination at the basin-level and income diversification is recommended. [ABSTRACT FROM AUTHOR]

Agriculture in sub-Saharan Africa remains highly vulnerable to climate related shocks, since most production relies on rainfall. It is important to accurately measure the resilience of farmers and farming communities to weather variabilities, for both government policy and farmer management responses. This paper develops a Resilience Index Framework, which is further used to assess the resilience of farmers to climate shocks in Nigeria. We conceptualized our Resilience Index (RI) in this study to be a composite function of 60 indicators encompassing four resilience domains namely, Economic & Financial Resilience (ER); Technical-know-how Resilience (TR); Social Resilience (SR); and Physical Resilience (PR). A three-stage standardization approach to construct the resilience index is taken in this study. In the first stage, each indicator is standardized. In the second stage, the resilience domain is computed by averaging the corresponding standardized indicators. In the final stage, the composite RI is computed by estimating the weighted average of all the resilience domains. The study uses the baseline survey data collected between 2021 and 2022 from a total of 5954 farmers in the rainforest, derived and guinea savannah agroecological zones of Nigeria. The result of the study shows that the majority (96.5%) of the farmers are less resilient to climate shocks, with only 0.9% economically & financially resilient, 1.4% socially resilient, 31.4% technically resilient, and 18.5% physically resilient. Finally, some recommend steps to be taken by the government and relevant stakeholders to improve the resilience of farmers through provision of good infrastructural facilities and subsidized improved resistant seed varieties are proposed. • It is important to accurately measure the resilience of farmers and farming communities to weather variabilities. • The designed resilience index measurement framework captures 60 indicators and adopted three standardization levels. • The result shows that the majority (96.5%) of the farmers in the study area are less resilient to climate related shocks. • The study suggests strengthening farming communities' adaptive capabilities to future extreme weather occurrences. [ABSTRACT FROM AUTHOR]

The presence of trace elements in water for domestic supply or irrigation could pose a significant toxic risk for health, due to direct consumption or bioaccumulation through the ingestion of vegetables irrigated with this water. This paper studies the presence of 41 trace elements plus nitrate and bromate in groundwater, using a multivariate statistical tool based on Principal Component Analysis and a geostatistical Kriging method to map the results. Principal Component Analysis revealed 11 significant principal components, which account for 82% and 81% of the total variance (information) respectively for the two dates analysed. Ordinary Kriging was applied to draw maps of the trace elements and PC scores. This research breaks new ground in terms of the large number of parameters used and in terms of the analysis of spatiotemporal variations in these parameters. The results obtained indicate that PC1 represents the natural quality of the aquifer (geogenic) and that there is little change in the average PC1 value between the two dates studied (June near the peak recharge point and November at the end of summer). Agriculture is the human activity that causes the greatest variations in the quality of the groundwater due to the use of fertilizers and due to watering crops with wastewater (PC7_J and PC5_N, June and November, respectively). Other elements of industrial origin, which are dangerous for human health, such as Pb, Cu and Cd, are grouped together in other principal components. The results show that the decline, or even complete absence, of natural recharge during the summer months leads to an increase in the TEs produced by human activity. This indicates that a temporary reduction in the natural recharge could worsen the quality of water resources. Based on the interpretation of the estimated maps, a synthetic map was created to show the spatial distribution of the areas affected by geogenic and anthropogenic factors. Studies with a global approach like this one are necessary in that the possible sources of pollution that could alter the quality of the groundwater and the amount of trace elements and other potentially harmful substances could increase as time goes by. The main advantage of the methodology proposed here is that it reduces the number of parameters, so simplifying the results. This makes it easier to interpret the results and manage the quality of the water. • PCA and geostatistical methods were used to establish the spatiotemporal variations in 41 trace elements. • A complex spatial relationship between anthropogenic and geogenic activities was reduced to just six categories. • The anthropogenic activity with the greatest impact on groundwater quality is agriculture. • Evaporitic rocks were the main cause of geogenic contamination of the groundwater. [ABSTRACT FROM AUTHOR]

Organophosphonates (OPs) are a unique group of natural and synthetic compounds, characterised by the presence of a stable, hard-to-cleave bond between the carbon and phosphorus atoms. OPs exhibit high resistance to abiotic degradation, excellent chelating properties and high biological activity. Despite the huge and increasing scale of OP production and use worldwide, little is known about their transportation and fate in the environment. Available data are dominated by information concerning the most recognised organophosphonate – the herbicide glyphosate - while other OPs have received little attention. In this paper, a comprehensive review of the current state of knowledge about natural and artificial OPs is presented (including glyphosate). Based on the available literature, a number of knowledge gaps have been identified that need to be filled in order to understand the environmental effects of these abundant compounds. Special attention has been given to GHG-related processes, with a particular focus on CH 4. This stems from the recent discovery of OP-dependent CH 4 production in aqueous environments under aerobic conditions. The process has changed the perception of the biogeochemical cycle of CH 4, since it was previously thought that biological methane formation was only possible under anaerobic conditions. However, there is a lack of knowledge on whether OP-associated methane is also formed in soils. Moreover, it remains unclear whether anthropogenic OPs affect the CH 4 cycle, a concern of significant importance in the context of the increasing rate of global warming. The literature examined in this review also calls for additional research into the date of OPs in waste and sewage and in their impact on environmental microbiomes. [Display omitted] • Artificial organophosphonates (OPs) are commonly released into the environment. • The degradation patterns and environmental impact of artificial OPs are unknown. • Knowledge about the impact of OPs on GHG emission is limited. • The release of OPs into the environment may require legislative actions. [ABSTRACT FROM AUTHOR]

The licensed cannabis industry represents one of the top five most economically valued agricultural commodities in California, yet farming largely remains on remote, environmentally sensitive, "marginal" lands. Using mixed methods, this paper examines the determinants of this marginalization, their embedded elaboration, and their relation to historical policy regimes. We used Generalized Additive Models (GAMs) to determine the most important predictors of licensed cannabis industry development since the inception of a statewide licensing program in 2018 and to compare the distribution of licensed cannabis to other forms of rural agriculture, including vineyards and pasture, to understand landscape factors and environmental sensitivity of land uses. We found that a county's median income and the extent of traditional (non-cannabis) agriculture, as measured by the proportion of on-farm (non-cannabis) employment, were both negatively associated with its amount of licensed cannabis agriculture. Ethnographic data suggests that cannabis is often excluded from traditional agricultural areas, through formal local-level bans, restrictive zoning, high "prime" farmland values, and cultural exclusions from other powerful resource users. The resulting relegation to "marginal" lands foments conflicts with amenity land users and environmentalists, even as it partly supports "legacy" cultivators whose farms were established under prior policy regimes. Results suggest that cannabis is more likely to be grown under conditions that introduce regulatory hurdles, including farming on steeper slopes, with natural streams onsite, and without access to large groundwater aquifers for irrigation. Our findings suggest that failure to allow licensed cannabis farming in traditional agriculture regions has led to a self-fulfilling prophecy wherein cannabis cultivation is largely relegated to environmentally sensitive areas where cultivation activity has an elevated tendency for environmental impacts. • Cannabis cultivation occurs outside conventional agricultural areas on "marginal" land. • Counties with higher incomes and more traditional agriculture issue fewer licenses. • Non-cannabis land users drive cannabis licensing restrictions. • Marginalization of cannabis produces environmental conflict and marginalizes farmers. • Marginal land use is socially and geographically produced. [ABSTRACT FROM AUTHOR]

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening—energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process. • Green innovation resilience can promote energy efficiency. • Early digital economy development can lead to resource grabbing and technology constraints effects. • Pursuing the policy orientation of "digital greening—energy efficient". [ABSTRACT FROM AUTHOR]

While socioeconomic and institutional factors are crucial in explaining the onset and evolution of conflicts, recent research suggests that climate change is a further indirect driver acting as a "threat multiplier". This paper focuses on the concept of vulnerability to both climate change and conflicts to explain why some locations are more likely to engage in armed conflicts than others in the presence of a similar level of exposure to climatic changes. In particular, by means of a Spatial Autoregressive Model, we identify a set of local-specific vulnerability factors that increase conflict risk in East Africa. We employ a georeferenced database with a resolution of 25 × 25 km, covering the period 1997–2016. Results from our analysis provide some interesting insights: first, climate change does not increase conflict risk per se , but only in the presence of pre-existing vulnerabilities. Second, resource access and socioeconomic factors play a key role in driving the climate-conflict nexus especially in urban areas. In particular, vulnerability is increased whenever power is not distributed in such a way as to ensure an equitable distribution of resources. Overall, our findings suggest that, by addressing vulnerability factors that prevent adaptive capacity and an equitable distribution of resources, societies may benefit in terms of both diminished conflict risk and alleviation of climate change impacts. • The climate-conflict nexus is mediated by access to resources at the local level. • Climate change only increases conflict risk in case of pre-existing vulnerabilities. • Climate change acts as a threat multiplier for vulnerable communities. • Synergies for ensuring adaptive capacity to climate change and peace exist. [ABSTRACT FROM AUTHOR]

With increasing internal and external risks to the WEF system, a single emphasis on efficiency or a lopsided pursuit of resilience can lead to difficulties in adapting to complex changes and resource redundancy. Revealing the synergistic evolutionary characteristics between efficiency and resilience of the WEF system is an effective method to deal with systemic internal and external risks. However, the current study of the WEF system lacks a synergistic perspective on resilience and efficiency. Thus, taking Chengdu-Chongqing Economic Circle (CCEC) as the research object and its geospatial boundary as the system boundary, this paper adopted the entropy-topsis model to evaluate the WEF resilience, and applied the super-efficient SBM model to measure the WEF efficiency accurately, which fully considered the non-expected outputs in the process of resource utilization. Then, applying the development coordination degree model, the synergistic relationship between the two was measured. The results indicated that: the average value of WEF resilience in CCEC increased from 0.414 to 0.485 and showed spatial characteristics of west＞east＞central. The WEF efficiency interval was 0.79–0.93, and cities with average WEF efficiency reaching the effective production frontier accounted for only 37.5%. The clustered distribution of the synergy levels intensified. The number of cities with primary, medium, more advanced, and advanced levels was 6, 6, 1, and 3, respectively, with primary and medium synergy levels dominating. The findings suggest that cities should strengthen regional exchanges and formulate targeted measures based on their own situations. In addition, CCEC should possess a comprehensive understanding of the interdependencies and conflicts that arise between resilience and efficiency throughout the decision-making procedure. [Display omitted] • Described the resilience mechanism of the WEF system and measured the WEF resilience. • Analyzed the synergistic relationship between resilience and efficiency of WEF system. • There was a competitive and cooperative relationship between WEF resilience and WEF efficiency. • It provides a new perspective for the sustainable development of the WEF system. [ABSTRACT FROM AUTHOR]

Interactions of microplastics (MPs) biofilm with antibiotic resistance genes (ARGs) and antibiotics in aquatic environments have made microplastic biofilm an issue of keen scholarly interest. The process of biofilm formation and the degree of ARGs enrichment in the presence of antibiotic-selective pressure and the impact on the microbial community need to be further investigated. In this paper, the selective pressure of ciprofloxacin (CIP) and illumination conditions were investigated to affect the physicochemical properties, biomass, and extracellular polymer secretion of polyvinyl chloride (PVC) microplastic biofilm. In addition, relative copy numbers of nine ARGs were analyzed by real-time quantitative polymerase chain reaction (qPCR). In the presence of CIP, microorganisms in the water and microplastic biofilm were more inclined to carry associated ARGs (2–3 times higher), which had a contributing effect on ARGs enrichment. The process of pre-microplastic biofilm formation might have an inhibitory effect on ARGs (total relative abundance up to 0.151) transfer and proliferation compared to the surrounding water (total relative abundance up to 0.488). However, in the presence of CIP stress, microplastic biofilm maintained the abundance of ARGs (from 0.151 to 0.149) better compared to the surrounding water (from 0.488 to 0.386). Therefore, microplastic biofilm act as abundance buffer island of ARGs stabilizing the concentration of ARGs. In addition, high-throughput analyses showed the presence of antibiotic-resistant (Pseudomonas) and pathogenic (Vibrio) microorganisms in biofilm under different conditions. The above research deepens our understanding of ARGs enrichment in biofilm and provides important insights into the ecological risks of interactions between ARGs, antibiotics, and microplastic biofilm. [Display omitted] • Characteristics of PVC biofilm formation by different conditions were summarized. • Selective pressure of CIP had enrichment effect on ARGs in water and biofilm. • PVC biofilm created conditions for selective pressure to generate ARGs. • Biofilm maintained the abundance of ARGs better than the surrounding water. • Characterization of bacterial community composition in PVC biofilm was studied. [ABSTRACT FROM AUTHOR]

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55–0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning. • ML simulator of specific flood volume as an alternative to the SWMM model. • Multi-criteria selection of machine learning method for flooding simulation. • Sobol indices prediction in GSA via empirical models. • Correlation between Sobol indices and characteristic of the catchment and sewer. [ABSTRACT FROM AUTHOR]

Climate change is a non-traditional security crisis affecting the global economy and diplomatic progress. In order to curtail carbon emissions and alleviate the perils of climate change at their roots, urban green innovation (UGI) has emerged as a pivotal technological solution. Using the expansion of the Yangtze River Delta urban agglomeration in China as a case study, this paper develops a quasi-experimental model to analyze the effects of regional integration policies on UGI. The main findings are: (1) Regional integration policies significantly enhance UGI and their impact is more pronounced with the expansion of urban agglomerations; (2) Regional integration policies contribute to the advancement of exploitative green innovation while tending to diminish exploratory green innovation; (3) The green innovation effects (GIEs) created by the expansion of regional integration policies are largely influenced by governmental mechanisms on environmental governance as well as residents' green preferences. Based on these findings, recommendations are put forward to promote UGI from the perspective of policy implementation. • The quasi-experimental models of urban agglomeration expansion on green innovation are constructed. • The positive effects of urban agglomeration expansion on green innovation are proven. • Urban agglomeration expansion can enhance exploitative green innovation and also reduce exploratory green innovation. [ABSTRACT FROM AUTHOR]

Loess, a terrestrial clastic sediment, is formed essentially by the accumulation of wind-blown dust, while stone waste (SW) is an industrial waste produced during stone machining. Utilising loess and SW to prepare environmentally-friendly supplementary cementitious materials can not only address environmental issues caused by solid waste landfills but also meet the demand of reinforcement of coal-seam floor aquifer for grouting materials. In this paper, the effects of the loess/SW mass ratio and calcination temperature on the transformation of calcined products are investigated and their pozzolanic activities are evaluated. The workability, environmental impact and cost of grouting materials based on cement and calcined products are also assessed. Experimental results reveal that higher temperatures favour the formation of free lime and periclase, which tend to be involved in solid-state reactions. Higher temperature and loess/SW mass ratio strengthens the diffraction peaks of dodecalcium hepta-aluminate (C 12 A 7), dicalcium ferrite (C 2 F) and dicalcium silicate (C 2 S). The clay minerals in loess become completely dehydroxylated before 825 °C, generating amorphous SiO 2 and Al 2 O 3. Covalent Si–O bonds are interrupted and that disordered silicate networks are generated in the calcined products, which is confirmed by the increased strength of the Si29 resonance region at −60 ppm to −80 ppm. Although co-calcined loess and SW contain the most four-fold aluminium at 950 °C, recrystallisation depresses the pozzolanic activity. Hence, the loess/SW sample designated LS 2-825 exhibits the better hydration activity. Additionally, grouting materials composed of cement and LS 2-825 exhibit good setting times, fluidity, strength and a low carbon footprint in practical engineering applications, and they also provide the additional benefit of being cost effective. • Utilising waste stone and loess to prepare SCMs was proposed. • The effect of temperature on co-calcined waste stone-loess was investigated. • The influence of mass ratio on co-calcined waste stone-loess was explored. • Grouting materials with calcined LS 2-825 possess excellent workability. [ABSTRACT FROM AUTHOR]

Utilising cardboard waste for the partial substitution of cement within concrete has the potential to yield significant sustainability benefits. Cardboard waste is abundantly available, and a significant proportion of this material is disposed of in landfill. However, conversion of waste cardboard into kraft fibres (KFs) for concrete implementation can be utilised in the building and construction industry. Therefore, identification of sustainability variables associated with cardboard waste in concrete is vital. In this study, two KF composites satisfied the criteria for low stress grade concrete and were subsequently evaluated. SFKF5 mix design contained 5% KFs and SFKF105 contained 10% KFs with 5% metakaolin (MK). Both composites had silica fume (SF) as a fibre modification technique for durability purposes. A life cycle assessment (LCA) determined the environmental effect of waste cardboard integration. A Monte-Carlo simulation was utilised as the sensitivity analysis to investigate transportation and energy manufacturing greenhouse gas (GHG) emission variables. LCA results of SFKF105 had a savings of 11%, 8%, 4% and 1% for terrestrial acidification potential, global warming potential (GWP), terrestrial ecotoxicity potential (TEP) and human toxicity potential, respectively. SFKF5 revealed savings of 3%, 2% and 4% for GWP, TEP and marine eutrophication potential, respectively. The additional travel requirements of KFs and MK to the cement batching plant for composite production did not surpass the embodied energy and travel emissions of the control. However, this was negated due to the additional energy requirements to manufacture KFs. The control, SFKF5, and SFKF105 had an average total of 572, 1023 and 997 kgCO 2 -eq/m3, respectively. • 5% cement replacement with kraft fibres can be utilised in low stress grade concrete. • 15% cement replacement in 1 m3 of concrete can save 8% greenhouse gas emissions. • 42 kgCO 2 -eq/m3 is saved when supplementing 15% cement in concrete. • Reducing landfill of waste cardboard can be achieved via concrete integration. • There are environmental benefits of redirecting waste cardboard from landfill areas. [ABSTRACT FROM AUTHOR]

Power-to-Gas (P2G) is considered as a promising energy storage technology in a long-time horizon. The rapid growth in the share of intermittent renewables in the energy mix is driving forward research and development in large-scale energy storage. This paper presents a feasibility analysis of a power-to-gas system in terms of various operating points and capacities. The analysis was performed using a system model, which features a solid oxide electrolyzer (SOE), a CO 2 separation unit, and a methanation reactor as the key components. For the purposes of the techno-economic assessment (TEA) of the system, the CAPEX/OPEX estimation was performed and the cost structure defined. The model proposed in the study enables system-level optimization, including technical and economic criteria, considering two nominal scales: 10 kW and 40 GW, which corresponds to the nominal capacity of SOE in each case. According to the study, in an SOE-based P2G system, the cost of synthetic natural gas (SNG) production will fall by 15–21% by 2030 and 29–37% by 2050. SNG production would cost 3.15–3.75 EUR 2023 /kg SNG in 2030 and 2.6–3.0 EUR 2023 /kg SNG in 2050 for systems with SOE power >10 MW. Generally, product cost reductions occur as a result of material development and large-scale production, which influences the system's CAPEX. According to the research, the technology will break even by 2050. The large-scale power-to-gas system with a total of 40 GW installed capacity delivers a product price of 2.4 EUR 2023 /kg SNG with the average conversion efficiency of 68%. • Feasibility study of Power-to-Gas system based on solid oxide electrolysis. • Multiparametric techno-economic simulation model has been developed. • Unique methodology with techno-economic assessment is proposed. • 40 GW system can deliver 2.4 EUR 2023 /kg SNG at conversion efficiency of 68%. [ABSTRACT FROM AUTHOR]

Spatial misallocation of resource elements impedes the coordination in regional co-benefit in carbon emission and pollution reduction. Guided by scope economy principles, this study aims to quantifies co-benefits and explores spatial variation patterns to facilitate mutual synergy. The findings offer valuable insights for identifying optimal focus points and shaping targeted collaborative policies. Based on the multi-source provincial panel data spanning 2000 to 2021, this paper quantitatively assesses the co-benefit through the lens of marginal abatement cost. Then, the Dagum Gini coefficient was employed to investigate the disparities at the spatial level. Additionally, geographical detector is introduced to analyze the source of disparities at the factor level. Results indicate: (1) Under joint reduction, the marginal abatement cost reduces by 57.86% and 79.97% respectively, with an overall 68.77% increase in co-benefit fluctuation. (2) Provinces with low co-benefit significantly decreased after 2007, while provinces with high co-benefit concentrated in the northwest, southwest, and east. (3) Overall disparities decreased, east-central regions hold the lowest spatial disparities in co-benefit and east-west hold the most; intragroup differences and supervariable density primarily contributes to the disparity. (4) FDI significantly influence the co-benefit over the past two decades, with spatial disparities influenced by both endogenous and exogenous factors across development stages. • Joint reduction significantly lowers the marginal abatement cost and enhanced co-benefit. • East-central regions holds the lowest spatial disparity in co-benefit and east-west holds the most. • Intragroup disparities and supervariable density primarily contributed to spatial disparity. • The interplay between internal and external factors increasingly influences spatial disparities. [ABSTRACT FROM AUTHOR]

Natural soundscape quality (NSQ) has been recognized as an essential cultural ecosystem service that contributes significantly to human health and well-being. It also stands as an indispensable component of environmental quality, especially for landscape aesthetic quality. However, an assessment tool for NSQ in landscape planning and environmental impact assessments is still absent. Therefore, this paper aims to address this gap by proposing an indicator-based model for assessing and quantifying NSQ in the Geographic Information System. The model characterizes NSQ based on Calmness and Vibrancy, and employs several indicators, sub-indicators, and respective metrics as proxies to quantify and map them spatially. The evaluation criteria of the model correspond to the general public's preferences for soundscape features. The case study results in Springe municipality, Germany, show that the relative values of NSQ are high in green spaces, including forests, grasslands, and shrublands, whereas they are low in open farmlands. The multiple natural sounds yield higher NSQ scores than the individual ones. The same soundscape compositions in forests and in urban parks exhibit higher NSQ scores than in other land cover types. In addition, the shares of relative values show similar distribution patterns among Calmness, Vibrancy, and NSQ according to land cover types and soundscape compositions. The evaluation results align with public values and preferences for soundscape features. Unlike subjectivist approaches, our user-independent methodology is easily transferable and reproducible. The results are comparable and communicable among the assessed areas. These endow the indicator-based model with the potential to be applied at various planning and management scales. The findings can help to incorporate soundscape evaluation into landscape planning and management systems, supporting sustainable landscape development, and providing valuable information for policy-, plan- and decision-making. [Display omitted] • Natural soundscape quality (NSQ) is assessed as a cultural ecosystem service. • An indicator-based model with evaluation criteria is developed for mapping and assessing NSQ. • The model is implemented in a municipality at a supralocal scale. • Mapping results are indicative, comparable, and communicable among the evaluated areas. • The methodology helps to integrate soundscape assessments into landscape planning and management. [ABSTRACT FROM AUTHOR]

Numerous studies have discussed the economic impacts of the COVID-19 pandemic in recent years. However, the effectiveness and trade-offs of diverse countermeasures still need to be investigated, particularly under the long-term goal of low-carbon transition, which is crucial for understanding the potential impacts of the future public health emergency (PHE) related economic crisis. Given that China still faces big pressures from the potential PHE and carbon neutrality, this paper assesses the effectiveness of policy instruments in restoring the economy and advancing green development after the PHE using the Dynamic Stochastic General Equilibrium framework. Our findings reveal that the PHE imposes more constraints on the economy because of the decrease in productivity on the supply side and in consumption on the demand side. Compared to the other counterparts, the mixed stimulus can overcome the adverse impacts of the PHE while contributing to carbon reduction. Furthermore, all types of low-carbon policies investigated in this study can contribute to carbon reduction at the expense of economic growth. Meanwhile, the carbon tax realizes the target of reducing emissions with the smallest negative impact on economic growth. Thus, we suggest adopting the carbon tax policy as the most effective low-carbon measure to address uncertainties associated with the PHE. • This study explores pathways for advancing economic recovery and green development after the public health emergency. • We develop a scenario-based dynamic stochastic general equilibrium (DSGE) model. • We characterize public health emergency shocks from a supply and demand perspective. • The model introduces different carbon abatement mechanisms. [ABSTRACT FROM AUTHOR]

The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2 ° C , has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems. • Discussed the advantages and limitations of current renewable energy systems. • Reviewed the different machine learning algorithms and their applications in renewable energy systems. • Highlighted existing renewable energy datasets and feature selection methods. • Discussed the use of machine learning and renewable energy in the hydrogen value chain. • Provided future directions for the application of machine learning in the renewable energy sector. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning. • Data-driven and physical modelling are merged to predict runoff. • Data-driven algorithm fed on weather variables before the storm starts. • Light GBM and Deep Neural Networks were coupled with Green-Ampt model. [ABSTRACT FROM AUTHOR]

Focusing on the European context, this paper re-examines the relationship between the ESG and financial performance and whether CSR attitude moderates this relationship. A panel data set with all the listed companies in STOXX Europe 600, covering the period 2012–2022 was built, with company data being sourced from the Refinitiv Eikon platform for a total of 6600 firm-year observations. Six measures of financial performance and three regression frameworks were considered for analysis. Overall, inconsistent patterns of effect are identified across ESG predictors and financial performance measures. The presence of a CSR committee was found to negatively moderate the effect of ESG score on ROA only, whereas an external auditor to the CSR report to negatively moderate it with share price. Future research should consider replicating the proposed re-examination framework in other data settings and legislative and accounting contexts to strengthen the current evidence base and therefore deepen our understanding of the mechanisms underlying the investigated relationships. • ESG scores affect financial performance measures inconsistently. • A CSR committee negatively moderates the effect of ESG score on ROA. • An external auditor negatively moderates the effect of ESG score on share price. [ABSTRACT FROM AUTHOR]

Curbing the continuous abandonment of large areas of farmland is important for meeting the global food demand and promoting agricultural and rural development. Accurate identification is the key to the effective management and utilization of abandoned farmland. The identification of abandoned land based on a long time series of remote sensing data has become rapid and effective. Therefore, a set of training and test datasets generated from invariant samples and reference sample sets is established in this paper. On this basis, the Google Earth Engine (GEE) is used to classify Landsat and Sentinel high-precision long-term remote sensing images from 2000 to 2022. In addition, a change detector based on the sliding window algorithm is proposed to extract abandoned farmland in the Huangshui Basin from 2002 to 2020, and the intensity, trend, frequency, reclamation rate and utilization efficiency are analyzed. The results revealed that the OA of land use classification in the Huangshui Basin from 2000 to 2022 was between 0.852 and 0.91, and the kappa coefficient was between 0.822 and 0.89, indicating a good classification effect. From 2002 to 2020, the accumulated abandoned farmland area in the Huangshui Basin continued to increase, showing a fluctuating upward trend, and the phenomenon of farmland abandonment and reclamation occurs repeatedly in some areas. From the overall distribution, the abandoned area gradually increased from the central region to the southeast. With the passage of time, the amount of abandoned farmland in the valley increased gradually, and the abandoned area was transferred from the high mountains to the valley area. The average annual abandonment rate of supplementary farmland was 50.45%, which was much greater than that of basic farmland. Most of the supplementary farmland could not be effectively and judiciously used, and the utilization efficiency was low. The research results provide data support for the reuse of abandoned farmland in ecologically fragile plateau areas, the formulation of targeted strategies, the implementation of timely adjustments, and the establishment of new ideas and methods for the accurate identification of abandoned farmland. • Developed a set of training and test data sets generated by invariant and reference samples. • Proposed a sliding window algorithm based on the change detector to plot long time series abandoned farmland. • Spatiotemporal distribution, trend, and recultivation of abandoned were analyzed. • Abandonment rate of supplementary farmland is higher than original farmland. [ABSTRACT FROM AUTHOR]

Urban forests as nature-based solutions (UF–NbS) play a crucial role for promoting the health and well-being of urban populations. Despite increasing recognition of their importance, knowledge gaps remain regarding UF-NbS implementation, particularly with respect to their performance, their perception, and on decision-making and governance practices for their implementation and management. Grounded in a project-as-practice approach and based on sixteen UF-NbS case histories, this paper seeks to gain insights into UF-NbS project actualities by an identification of characteristic trends as a function of a projects' scope and purpose, or challenge-orientation, respectively. A particular emphasis is on project structure and coordination, on the scope and depth of participation and stakeholder involvement, and on funding mechanisms. Identified trends reveal centrally coordinated, single-responsible public authorities as the most common project structure, but with certain feature contents, i.e., regeneration and reclamation, as being associated with more decentralised forms of governance. It is concluded that irrespective of challenge-orientation, comparatively broad participation appears to be realised within the UF-NbS framework, however, the depth of participation differs considerably. With respect to funding, a strong reliance on public capital is asserted for the UF-NbS context, however, various forms of private contributions appear reasonably common across the assessed case histories. Recommendations for policy-making and avenues of future research are identified, including the validation of identified trends, the fostering of collaboration, the dispersal of decision-making authority, the encouragement for implementing non-tokenistic modes of participation in UF-NbS governance, and the promotion of potentially under-utilised funds. • The governance of urban forests as nature-based solutions has been investigated. • Analysis of sixteen case histories grounded in a project-as-practice approach. • Identification of associations of governance features with challenge-orientations. • Recommendations for policymaking to improve the governance of urban forests. • Avenues for future research highlighted to promote project-based knowledge exchange. [ABSTRACT FROM AUTHOR]

Foreign direct investment benefits developing countries. However, concerns have arisen that the influx of FDI potentially exacerbates environmental pollution. While this debate continues, growing attention has recently emerged on the role of institutions in mitigating FDI's potential damages, although the empirical findings remain inconclusive. This paper examines how institutional quality shapes the relationship between FDI and CO 2 , both at the aggregate level and across different income groupings, using a reduced-form CO 2 emissions model, panel data from 2000 to 2018 and the IVGMM techniques. Three key conclusions emerge. First, the findings show that FDI reduces CO 2 emissions, but its magnitude depends on the measure used. Second, institutional quality is directly associated with higher emissions across income groups, suggesting current regulations inadequately ameliorate environmental pollution. Third, we find a positive interaction effect between CO 2 emissions and institutional quality. We argue that, for FDI to consistently curb CO 2 emissions, the quality of institutions must improve to better regulate foreign investors' activities, especially in low and high-income nations. Enhancing the quality of institutions will help translate FDI into improved environmental outcomes across income groups. • This study investigates how institutional quality shapes the relationship between FDI and CO 2 emissions. • The findings indicate that institutional quality inadequately ameliorates CO 2 emissions. • The findings show a positive interaction effect between CO 2 emissions and institutional quality. • To ensure FDI reduces CO 2 emissions, institutions must regulate foreign investors' activities in both low and high-income nations. [ABSTRACT FROM AUTHOR]

In wastewater treatment plants (WWTPs), the stochastic nature of influent wastewater and operational and weather conditions cause fluctuations in effluent quality. Data-driven models can forecast effluent quality a few hours ahead as a response to the influent characteristics, providing enough time to adjust system operations and avoid undesired consequences. However, existing data for training models are often incomplete and contain missing values. On the other hand, collecting additional data by installing new sensors is costly. The trade-off between using existing incomplete data and collecting costly new data results in three data challenges faced when developing data-driven WWTP effluent forecasters. These challenges are to determine important variables to be measured, the minimum number of required data instances, and the maximum percentage of tolerable missing values that do not impede the development of an accurate model. As these issues are not discussed in previous studies, in this research, for the first time, a comprehensive analysis is done to provide answers to these challenges. Another issue that arises in all data-driven modeling is how to select an appropriate forecasting model. This paper addresses these issues by first testing nine machine learning models on data collected from three wastewater treatment plants located in Iran, Australia, and Spain. The most accurate forecaster, Bayesian network, was then used to address the articulated challenges. Key variables in forecasting effluent characteristics were flow rate, total suspended solids, electrical conductivity, phosphorus compounds, wastewater temperature, and air temperature. A minimum of 250 samples was needed during the model training to achieve a great reduction in the forecasting error. Moreover, a steep increase in the error was observed should the portion of missing values exceed 10%. The results assist plant managers in estimating the necessary data collection effort to obtain an accurate forecaster, contributing to the quality of the effluent. [Display omitted] • Bayesian network is benchmarked against eight AI models to predict wastewater quality. • Bayesian networks can handle missing value in input data due to probabilistic nature. • Key variables in WWTP quality and quantity were specified. • Input data quality has a huge role in successful development of data-driven models. • The prediction error sharply increases when the portion of missing data exceeds 10%. [ABSTRACT FROM AUTHOR]

This paper addresses the critical environmental issue of effectively managing construction and demolition waste (CDW), which has seen a global surge due to rapid urbanization. With the advent of deep learning-based computer vision, this study focuses on improving intelligent identification of valuable recyclables from cluttered and heterogeneous CDW streams in material recovery facilities (MRFs) by optimally leveraging both visual and spatial features (depth). A high-quality CDW RGB-D dataset was curated to capture MRF stream complexities often overlooked in prior studies, and comprises over 3500 images for each modality and more than 160,000 dense object instances of diverse CDW materials with high resource value. In contrast to former studies which directly concatenate RGB and depth features, this study introduces a new depth fusion strategy that utilizes computationally efficient convolutional operations at the end of the conventional waste segmentation architecture to effectively fuse colour and depth information. This avoids cross-modal interference and maximizes the use of distinct information present in the two different modalities. Despite the high clutter and diversity of waste objects, the proposed RGB-DL architecture achieves a 13% increase in segmentation accuracy and a 36% reduction in inference time when compared to the direct concatenation of features. The findings of this study emphasize the benefit of effectively incorporating geometrical features to complement visual cues. This approach helps to deal with the cluttered and varied nature of CDW streams, enhancing automated waste recognition accuracy to improve resource recovery in MRFs. This, in turn, promotes intelligent solid waste management for efficiently managing environmental concerns. [Display omitted] • Features a high-quality CDW RGB-D dataset curated to capture MRF stream complexities. • Direct feature fusion results in large feature maps and loss of distinct characteristics. • Advanced downstream fusion mitigates cross-modal interference. • Proposed RGB-D L fusion strategy improves accuracy without incurring high computational cost. • Most significant improvements are observed in small object segmentation. [ABSTRACT FROM AUTHOR]

Land subsidence induced by coal mining (MLS) has posed a huge threat to the ecological environment, buildings, roads, and other infrastructure safety in mining areas. However, the prediction and evaluation of MLS is relatively complex, and the reliability of the prediction results is closely related to factors such as the professional knowledge and engineering experience of researchers. This paper aims to combine intelligent optimization algorithms: ant lion optimizer (ALO), bald eagle search (BES), bird swarm algorithm (BSA), harris hawks optimization (HHO), and sparrow search algorithm (SSA), with machine learning model of gradient boosting with categorical features support algorithm (CatBoost) to predict MLS. To achieve this goal, five hybrid models based CatBoost were developed and the prediction accuracy and reliability of the models were compared and analyzed. The prediction performance of the hybrid models has been significantly improved on the basis of a single model, of which the SSA-CatBoost model has the most obvious improvement (from R2 = 0.927 to 0.965, RMSE = 0.541 to 0.377, MAE = 0.386 to 0.297, VAF = 92.720 to 95.837). The importance and predictive contribution of all input features to predictive labels were studied with the Shapley method. The research results indicate that hybrid model technology is a reliable MLS prediction method. This study can help mining technicians use machine learning methods to study the degree of MLS damage to the surface environment and provide scientific advanced prediction and evaluation for the protection and management of the ecological environment in mining areas and the formulation of safety production measures. • High-precision prediction model of Mining Land Subsidence (MLS) was proposed. • The intelligence algorithms were used to Automatically optimize the hyperparameters of CatBoost. • The hybrid model can more reliably and intelligently evaluate the degree of MLS harm. • The hybrid model can scientifically guide mining environmental protection. [ABSTRACT FROM AUTHOR]

Global warming has accentuated the effects of extreme heat on health. Health insurance, functioning as a risk management tool, has the potential to alleviate these impacts. Consequently, this paper investigates the correlation between extreme heat events and the demand for health insurance in China. Using data from the China Health and Nutrition Survey, we have observed a substantial increase in the likelihood of residents purchasing health insurance during extreme heat events. To be specific, for every extra day of extreme heat events annually, there is a 0.3% increase in the probability of purchasing health insurance. This effect is not uniform across different demographic groups. It is particularly pronounced among middle-aged and elderly individuals, rural residents, those with lower educational levels, higher income brackets, and individuals residing in underprivileged areas with limited access to green spaces and healthcare facilities. Furthermore, our study indicates that the increased frequency of extreme heat events not only impacts individuals' physical health but also triggers negative emotions, which in turn drive risk-averse behavior related to health insurance purchases. These findings carry substantial policy implications for mitigating the economic consequences of climate change. • Extreme heat increases the likelihood of residents purchasing health insurance. • Each additional day of extreme heat boosts health insurance purchases by 0.3%. • This effect varies among different populations. • Mechanistic analyses indicate extreme heat impacting physical and mental health. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for the adaptive reservoir operation considering water quantity and quality objectives. In this framework, using the European Centre for Medium-Range Weather Forecasts (ECMWF) database, the monthly precipitation forecasts, with up to 6-month lead time, are downscaled and bias corrected. The rainfall forecasts are used as inputs to a rainfall-runoff simulation model to predict sub-seasonal inflows to reservoir. The water storage at the end of a short-term planning horizon (e.g. 6 months) is obtained from some probabilistic optimal reservoir storage volume curves, which are developed using a long-term reservoir operation optimization model. The adaptive optimization model is linked with the CE-QUAL-W2 water quality simulation model to assess the quality of outflow from each gate as well as the in-reservoir water quality. At the first of each month, the inflow forecasts for the coming months are updated and operating policies for each gate are revised. To tackle the computational burden of the adaptive simulation-optimization model, it is run using Parallel Cellular Automata with Local Search (PCA-LS) optimization algorithm. To evaluate the applicability and efficiency of the framework, it is applied to the Karkheh dam, which is the largest reservoir in Iran. By comparing the run times of the PCA-LS and the Non-dominated Sorting Genetic Algorithms II (NSGA-II), it is shown that the computational time of PCA-LS is 95 % less than NSGA-II. According to the results, the difference between the objective function of the proposed adaptive optimization model and a perfect model, which uses the observed inflow data, is only 1.68 %. It shows the appropriate accuracy of the adaptive model and justifies using the proposed framework for the adaptive operation of reservoirs considering water quantity and quality objectives. • Adaptive reservoir operation model considering water quantity and quality objectives. • Using ECMWF-based rainfall forecasts with monthly time step and 6-month lead time. • Linking the optimization model with the CE-QUAL-W2 water quality simulation model. • Applying the real-time operation model to the Karkheh river-reservoir system in Iran. • Illustrating the efficiency of the proposed model in a real-world large-scale problem. [ABSTRACT FROM AUTHOR]

Blockchain Technology has garnered significant attention due to its immense potential to transform the way transactions are conducted and information is managed. Blockchain is a decentralized digital ledger that is spread across a network of computers, ensuring the secure, transparent, and unchangeable recording of transactions. However, the energy consumption of certain blockchain networks like Bitcoin, Litecoin, Monero, Zcash, and others has generated apprehensions regarding the sustainability of this technology. Bitcoin alone consumes approximately 100 terawatt-hours annually, contributing significantly to global carbon emissions. The substantial energy requirements not only contribute to carbon emissions but also pose a risk to the long-term viability of the blockchain industry. This study reviews articles from eight reputable databases between 2017 to August 2023, employing the systematic review and preferred reporting items for systematic reviews and meta-analyses approach for screening. Therefore, explore the applications of sustainable blockchain networks aimed at reducing environmental impact while ensuring efficiency and security. This survey also assesses the challenges and limitations posed by diverse blockchain applications regarding sustainability and provides valuable foresight into potential future advancements. Through this survey, the aim is to track and verify sustainable practices, facilitating the transition to a low-carbon economy, and promoting environmental stewardship, with a specific focus on highlighting the potential of sustainable blockchain networks in enabling secure and transparent tracking of these practices. Finally, the paper sheds light on pertinent research challenges and provides a roadmap of future directions, stimulating further research in this promising field. • Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA Study). • Identification of Sustainable Blockchain Use Cases. • Sustainable Blockchain Challenges and Opportunities. • Blockchain for Transparent Sustainability Tracking. • Future Research Directions. [ABSTRACT FROM AUTHOR]

The site selection for Low Impact Development (LID) practices is a significant process. It affects the effectiveness of LID in controlling stormwater surface runoff, volume, flow rate, and infiltration. This research paper presents a comprehensive review of various methods used for LID site selection. It starts by introducing different methods and tools. Three main methods: index-based methods, GIS-based multi-criteria decision analysis (MCDA), and multi-criteria models and tools, are discussed in detail. A comparative analysis of these methods is then conducted based on ten different criteria. These criteria include the number of variables, data properties, the scale of analysis, benefits maximization approach, multi-attribute decision analysis, user-friendliness, community and stakeholder participation, and the validation methods. This comparison reveals limitations in each method. These include inadequate data availability and quality, lack of evaluation methods, comprehensive assessment criteria and spatial explicitness. These challenges underscore the need for future research to prioritize spatial clarity, broaden criteria, improve data quality through standardization, incorporate field visits and remote sensing for robust results, integrate big data, and develop web-based, open-source tools for enhanced accessibility. These key strategies provide valuable insights for advancing LID site selection methods. • Reviews key LID site selection methods. • Comparative analysis based on multiple factors. • Highlights method limitations, prompting further research in LID site selection. • Recommends improvements and new methods to address research gaps. [ABSTRACT FROM AUTHOR]

This paper examines the socio-ecological resilience within China's Northeast National Forest Region (NNFR), focusing on the implications of climate change for forest management and carbon sequestration. It offers a critical assessment of the Natural Forest Protection Program (NFPP) and the associated logging ban policy, recognizing their pivotal contributions to forest conservation but also identifying the shortcomings of a one-size-fits-all approach. Integrating panarchy theory, the study proposes sustainable management practices that align ecological dynamics with societal needs, emphasizing nature-based solutions. The overarching aim is to bolster the long-term resilience and enhance the carbon sequestration potential of the NNFR's forests. It aims to inform global environmental strategy with lessons from the NNFR, advocating for integrated approaches that ensure both ecological sustainability and community prosperity. This approach seeks to provide a comprehensive and effective strategy for addressing environmental challenges, ensuring both ecological integrity and community well-being. • Current regulations of natural forests have threatened to exceed critical socio-ecological thresholds. • Prevailing studies on Northeast National Forest Region of China for socio-ecological system resilience are scarce. • Resilience thinking is required to tackle efficient natural resource management. • Application of Panarchy theory highlights the cross-scale interactions growth of coupling social and ecological sub-systems. • Nature-based solution could help restore socio-ecological resilience. [ABSTRACT FROM AUTHOR]

The release of uranium from uranium tailings into the aqueous environment is a complex process controlled by a series of interacting geochemical reactions. In this paper, uranium tailings from a uranium tailings pond in southern China were collected at different depths by means of borehole sampling and mixed to analyze the fugacity state of U. Static leaching experiments of U at different pH, oxidant concentration and solid-to-liquid ratios and dynamic leaching experiments of U at different pH were carried out, and the adsorption and desorption behaviour of U in five representative stratigraphic media were investigated. The results show that U is mainly present in the residue state in uranium tailings, that U release is strong in the lower pH range, that the leached U is mainly in the form of U(VI), mainly from the water-soluble, Fe/Mn oxides and exchangeable fraction of uranium tailings, and that the reduction in U leaching at higher pH is mainly due to the combined effect of precipitation formation and larger particle size of platelets in uranium tailings. Experiments with different oxidant concentrations and solid-liquid ratios showed that the oxygen-enriched state and low solid-liquid ratios were favorable for the leaching of U from uranium tailings. Adsorption and desorption experiments show that U is weakly adsorbed in representative strata, reversibly adsorbed, and that U is highly migratory in groundwater. The present research results have important guiding significance for the management of existing uranium tailings ponds and the control of U migration in groundwater, which is conducive to ensuring the long-term safety, stability and sustainability of uranium mining sites. [Display omitted] • The leaching of U from uranium tailings was tested under different geochemical conditions. • Adsorption behaviour of U in representative geotechnical media in uranium tailings pond areas. • The release of U from uranium tailings is mainly from the water-soluble state. • Low pH, oxygen enrichment and low solid to liquid ratio conditions favour the release of U from uranium tailings. [ABSTRACT FROM AUTHOR]

Population aging and global warming have become everyday concerns of all countries. Based on the panel data of 30 provinces in China from 2003 to 2019, this paper uses the panel fixed effect model and two-stage least square method to analyze the effect of population aging on domestic energy carbon emissions of urban and rural residents. On this basis, the threshold regression model is introduced to explore the heterogeneity of the effect under different aging levels. The results show that (1) the progress of population aging at the overall level will significantly increase the level of carbon emissions from household energy consumption. At the regional level, the effect of population aging on carbon emissions from household energy consumption in rural areas is higher than in urban areas. (2) Population aging has a nonlinear effect on the carbon emissions of residential energy consumption. For urban areas, when the level of population aging crosses the threshold, its marginal impact on living carbon emissions in urban areas is further enhanced. In contrast, the opposite is true in rural areas. (3) Heterogeneity analysis results show that the impact of population aging on residential energy carbon emissions differs in different regions at the national and rural levels but does not show regional heterogeneity at the urban level. [Display omitted] • Population aging will significantly increase the carbon emission level of residents' living energy consumption. • Higher impact of population aging on the carbon emissions of living energy consumption in rural areas. • Population aging has a nonlinear impact on the carbon emissions of residents' living energy consumption. • There is heterogeneity in the regression results among different regions. [ABSTRACT FROM AUTHOR]

The pace of species extinction and deforestation has increased dramatically due to the substantial increase in global environmental degradation. This trend is approaching the crucial temperature threshold of 2 °C and calls for more attention. Although previous research has observed the individual impacts of forest depletion, structural change, economic growth, and urbanization on various sustainability outcomes, there has been no previous research into their interrelationships with an emphasis on the load capacity factor (LCF). Furthermore, no previous study has examined the environmental impacts of the abovementioned variables by contrasting the results of LCF and CO2 emissions in Pakistan. Therefore, this research suggests a theoretical framework that integrates these concepts, provides a roadmap for an effective and sustainable mitigation strategy for Pakistan and compares LCF results with CO2 emissions. Using the time-series data from 1970 to 2021, a unique and sophisticated dynamic Autoregressive Distributed Lag (DARDL) technique, the authors found that (i) a 1 % rise in forest depletion leads to a decline in load capacity factor by 0.026 %. (ii) A one per cent upsurge in structural change fosters environmental sustainability by raising the load capacity factor by 0.084 %. (iii) An increase of 1 % in economic growth dwindles the load capacity factor by 0.027 %. (iv) A one per cent surge in urbanization enhances the load capacity factor by 0.029 %. The findings suggest that Pakistan's Government should promote afforestation by emphasizing the constructive role of structural change in achieving environmental sustainability. • This paper examined the impact of forest depletion and structural change on environment in Pakistan. • A novel dynamic ARDL estimator is applied for empirical assessment. • Forest depletion impedes environmental quality. • Structural changes foster a sustainable environment in Pakistan. [ABSTRACT FROM AUTHOR]

The construction and precise synthesis of materials based on functional and structural orientations have emerged as a pivotal platform in the field of environmental management. In this paper, an efficient and stable catalyst (RuLDH) was constructed to achieve this goal. RuLDH comprises individual Ru atoms that are uniformly dispersed on ZnAl-LDH, achieved by room temperature stirring. Remarkably, RuLDH exhibits exceptional performance under visible light, effectively triggering the photocatalytic degradation of tetracycline hydrochloride (TC) via peroxymonosulfate (PMS) with a remarkable efficiency of 100%, all while avoiding the generation of highly toxic intermediates. In addition, RuLDH0.2 demonstrated its utility in fluorescence detection of TC, showcasing commendable analytical performance characterized by rapid response, low detection limit, and robust resistance to environmental interferences (with a detection limit of 1.0 mg/L). Notably, the RuLDH0.2/PMS/Vis system exhibited remarkable efficacy in treating actual pesticide wastewater, effectively exerting bactericidal and disinfectant effects. This study serves as a source of inspiration for the design of multifunctional single-atom catalysts, thereby pushing the boundaries of "integration of diagnosis and treatment" in environmental management and control. [Display omitted] • Single-atom Ru dispersed on ZnAl-LDH was synthesized by stirring method. • Development of a single-atom oxidation technology with ultra-high oxidation activity. • Sensitive detection of TC is achieved with the RuLDH0.2 system. • The RuLDH0.2/PMS/Vis system has the potential to treat real pesticide wastewater. [ABSTRACT FROM AUTHOR]

The effective removal of heavy metal ions from sewage remains a critical issue, and applying the operability of magnetic materials to large volume wastewater treatment has been a significant challenge. In this paper, metal ions adsorption induced aggregation strategy is proposed to solve this contradiction. The intelligent magnetic fluid designed in this study is a well-dispersed fluid state when treating sewage, and can efficiently adsorb heavy metal ions in wastewater with high adsorption capacity and ultra-fast adsorption kinetics. More importantly, after saturation of adsorption, the magnetic fluid will transform from a well-dispersed fluid state to an agglomeration state which is easy to precipitate and separate via external magnetic field. In a simple and effective way, the particles size of magnetic nanoparticles was precisely controlled by cellulose derivatives modification to obtain a stable magnetic fluid in water. The Freundlich model best described Cu2+ adsorption on magnetite nanoparticles, the correlation coefficients from the Cu2+ adsorption on the two magnetic fluids are 0.9554 and 0.9336, n are 1.868 and 2.117, revealing a favorable adsorption of Cu2+ onto magnetic fluids. The pseudo second-order model fitted the adsorption kinetic data better, the q e are 0.1948 and 0.1315 mmol/g and the R2 are 0.9999, indicating that the adsorption of Cu2+ onto the magnetic fluid was dominated by chemisorption. Moreover, the removal rate of Cu2+ in tap water and lake water was more than 97.1%, and the removal rate of large volume sewage was 81.7%. The synthetic magnetic fluid has high adsorption capacity, ultra-fast adsorption kinetics, reusability and easy separation, indicating its potential application for the removal of heavy metal ions from large-volume sewage. [Display omitted] • The first attempt of magnetic materials for the treatment of large volume sewage. • This magnetic fluid has high adsorption capacity and ultra-fast adsorption kinetics. • Magnetic fluid transforms into agglomerated state after adsorption saturation. • The adsorbent can be easily separated and reused via external magnetic field. • The presence of heavy metals in water can be detected simultaneously by sedimentation. [ABSTRACT FROM AUTHOR]

This paper systematically and critically reviews the literature on the intersection of circular economy practices (CEPs) and sustainability performance (SP). We synthesize and analyze the extant literature to uncover the knowledge gaps, highlight the contradictory views, and provide a comprehensive overview of the field. Following a detailed database search, we selected 104 empirical studies published in peer-reviewed journals for analysis. Our review reports the publication trends, top publishing journal outlets, research methodologies, and empirical contexts. We outline the theoretical underpinnings, identify the diverse circular economy practices and the key factors that impact circular economy practices and sustainable performance. The review shows a propensity for most authors to reuse established theories or not use theory at all, revealing the need for theory development. Furthermore, our analysis revealed that R&D and innovation, digital technologies, organizational capabilities/resources, and stakeholder and institutional pressure substantially influence the CEPs - SP relationship. Through our detailed assessment of the existing literature, we identified and proposed several themes and avenues for future research. • Circular economy practices (CEPs) can lead to sustainability performance (SP). • What factors influence CEPs and SP? • CEPs can be classified into various categories, each serving distinct purposes. [ABSTRACT FROM AUTHOR]

Despite increasing interest, a lack of comprehensive knowledge regarding the efficient design and implementation of carbon farming schemes remains. These schemes must efficiently achieve higher carbon sequestration, incentivize farmers, and increase farmers' participation in global carbon markets. Our study systematically reviews, describes, and maps available evidence related to carbon farming contracts to assess different incentive mechanisms for carbon farming. We conduct a systematic mapping review of articles extracted from various databases employing the Collaboration for Environmental Evidence method. We shortlist 52 articles and analyze about 40 global case studies, identifying three main incentive mechanisms of carbon farming contracts, namely, result-based, action-based, and hybrid payments. We examine how these incentive mechanisms are designed, in addition to associated payment types, monitoring approaches, and barriers to implementation. Result-based payments include stringent monitoring and can be implemented through auctions, carbon credits, product labels or certificates. Action-based payments are found to be simpler, with lower monitoring requirements for farmers and can be paid upfront or after contract implementation. Hybrid payments combine both techniques, offering low-risk and guaranteed payments for farmers and definite environmental mitigation impacts. Result-based and hybrid payments motivate farmers to innovate to meet environmental objectives while also connecting them to carbon markets. The major challenges to developing a successful carbon farming project include lack of permanence, non-additionality, and the absence of stringent monitoring, reporting, and verification standards, all of which affect farmers' incentives. This study determines that carbon farming contract design and efficiency can be improved by analyzing the lessons learned from previous experiences. By examining and improving the attributes that define different incentive mechanisms, farmers can be better motivated to enroll in carbon farming schemes and benefit from increased access to carbon markets to potentially transform agriculture into a viable tool for climate action. • Systematic mapping reviewed 52 papers, which encompassed 40 global cases. • Three incentive mechanisms: result-based, action-based, and hybrid payments. • Results-based payments: stringent MRV, high risk but high profitability to farmers. • Action-based payments: simple MRV, low risk but low profitability to farmers. • Hybrid payment: low-risk and high profitability; connect farmers to carbon markets. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed. • Deep learning by combined CNN-LSTM networks for carbon price forecasting. • Hyperparameter tuning of deep learning models for improving forecast accuracy. • CNN-LSTM models outperform single CNN models and single LSTM models. • Performance metric of deep learning for abrupt changes in carbon price variation. [ABSTRACT FROM AUTHOR]

Models and metrics to measure ecological connectivity are now well-developed and widely used in research and applications to mitigate the ecological impacts of climate change and anthropogenic habitat loss. Despite the prevalent application of connectivity models, however, relatively little is known about the performance of these methods in predicting functional connectivity patterns and organism movement. Our goal in this paper was to compare different connectivity models in their abilities to predict a wide range of simulated animal movement patterns. We used the Pathwalker software to evaluate the performance of several connectivity model predictions based on graph theory, resistant kernels, and factorial least-cost paths. In addition, we assessed the efficacy of synoptic and patch-based approaches to defining source points for analysis. In total, we produced 28 different simulations of animal movement. As we expected, we found that the choice of connectivity model used was the variable that most influenced prediction accuracy. Moreover, we found that the resistant kernels approach consistently provided the strongest correlations to the simulated underlying movement processes. The results also suggested that the agent-based simulation approach itself can often be the best analytical framework to map functional connectivity for ecological research and conservation applications, given its biological realism and flexibility to implement combinations of movement mechanism, dispersal threshold, directional bias, destination bias and spatial composition of source locations for analysis. In doing so, we provide novel insights to guide future functional connectivity analyses. In future research, we could use the same model for several different species groups and see how this reliability depends on the species analyzed. This could bring to light other elements that play an essential role in predicting connectivity. • The connectivity method is what influences predictions the most. • Using a synoptic approach better predicts walker movements than a patch-based one. • Agent-based simulation approach may often be the best analytical framework to map functional connectivity. • Simulation model flexibility is a key factor in determining connectivity. • Resistant kernel-based has the best performance in predicting simulated movement patterns. [ABSTRACT FROM AUTHOR]

The world is currently facing urgent climate and environmental issues, such as global warming, ecosystem collapse, and energy shortages. In this context, this study selected data from 2000 to 2021 and employed the Method of Moment Quantile Regression (MMQR) to thoroughly investigate the impact of renewable energy consumption, economic complexity, and geopolitical risks on the ecological footprint of the Group of Twenty (G20) countries. The results indicate that in countries with lower quantiles, renewable energy consumption significantly reduces the ecological footprint, whereas its effect is not prominent in countries with higher quantiles. Economic complexity has a negative impact on the ecological footprint, and this impact becomes stronger as the quantile of the ecological footprint rises. Additionally, economic complexity moderates the effect of renewable energy on the ecological footprint. Geopolitical risks facilitate the growth of the ecological footprint. Likewise, robustness tests such as DOLS, FMOLS, and quantile regression confirm these estimates in the same framework. This study has conducted a profound analysis of global environmental issues, offering innovative perspectives and recommendations for achieving goals related to sustainable energy utilization, mitigating climate change, and improving the ecological environment. The findings of this research will guide policymakers in G20 countries to adopt more effective environmental protection measures, thereby contributing to the construction of a sustainable future. • This paper investigates the factors influencing the ecological footprint of G20 countries. • The study utilized a novel Method of Moment Quantile Regression to explore nexus among the study variables. • Renewable energy consumption has no significant effect on the ecological footprint in higher quantiles. • Economic complexity moderates the effect of renewable energy on the ecological footprint. • Geopolitical risks exhibit a unidirectional causality with the ecological footprint. [ABSTRACT FROM AUTHOR]

An increasing number of countries and regions consider the bioeconomy transition a strategic policy priority. When approached through the lens of a circular economy perspective, investments in bioeconomy have the potential to enhance resource utilisation efficiency, preserve biodiversity and ecosystems, and foster sustainable development with low emissions. At the same time, if requirements and contextual factors of bioeconomy strategies are not formally analysed, bioeconomic investments might lead to unintended negative consequences. This paper proposes a decision support procedure to design, assess, prioritise, and monitor bioeconomy investments and policies. The flexibility and scalability of our decision support procedure is tested in Colombia to foster a regional and local transition to bioeconomy initiatives that consider the local capital assets and the stakeholders' views. The heterogeneous character of the Colombian environment, economy, society and culture represents an ideal condition to test the strength of the decision support procedure to promote bioeconomy in low and middle-income countries. Our empirical results highlight the benefit of adopting a formal assessment framework that includes strategic national indicators, regional features and stakeholders' views. In terms of the Colombian regional bioeconomy ambitions, we highlight the need for expanding knowledge hubs and participatory stakeholder networks and buttressing appropriate financial mechanisms. • Bioeconomy transition needs to be framed in an extended circular economy framework. • Bioeconomy strategies need to consider local context and stakeholders' engagement. • Scalable and replicable decision support procedures should guide bioeconomy implementations. • Balance sheet approach inform all stages of bioeconomy policies and projects. • Application of the balance sheet approach in Colombia highlights diverse local priorities. [ABSTRACT FROM AUTHOR]

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals. • The current study analyses the role of green factor productivity in zero carbon transition. • We use 30 chinese provincial datasets from 2005 to 2020. • Energy transition is the most effective channel to improve green total factor productivity. [ABSTRACT FROM AUTHOR]

This paper aims to examine the impact of geopolitical risk (GPR), threats (GPT) and acts (GPA) on returns and volatilities of regional climate change stocks under different market conditions, employing quantile regressions. Our main results suggest that climate change stock returns positively (negatively) respond to GPR in bullish (bearish) market states, however the effect is not uniform across the regions. The volatilities mainly show a positive response to geopolitical tensions; geopolitical acts appear to have a more pronounced impact on volatilities than geopolitical threats. We further find that GPR leads to higher volatility during the Russia-Ukraine war, creating heightened uncertainty. Overall, the results reveal that geopolitical risks have an asymmetric and heterogenous impact on climate change stocks. The results provide significant insights and implications for financial market participants and policy makers. • The study investigates how geopolitical risks (GPR) influence the returns and volatilities of climate change stocks in different market conditions. • We focus on the Russia's invasion of Ukraine as a geopolitical conflict. • We find an asymmetric impact of GPR on climate change stock returns, with positive responses observed in bullish market states and negative responses in bearish market states. • While GPR does not significantly affect returns, it leads to higher volatilities during the Russia-Ukraine war. [ABSTRACT FROM AUTHOR]

This study investigates the impact of country governance mechanisms on carbon emissions performance of private sector organisations, using empirical evidence from 336 top multinational entities (MNEs) over a 15-year period. The results show that, at the aggregate level, Control of Corruption (b = −0.021, p < 0.01) and Voice & Accountability (b = −0.015, p < 0.05) are significantly and negatively associated with carbon emissions rate. While Political Stability (b = 0.007, p < 0.05) and Government Effectiveness (b = 0.018, p < 0.05) have significant positive impact on carbon emissions rate, the impact of Regulatory Quality and Rule of Law is negative but insignificant. Empirical evidence supports the conclusion that the existing institutional environment is not sufficient to deliver the net zero transition. There is a need for more coordination, strategic planning, and delivery monitoring in government institutions to achieve decarbonisation targets. The study contributes to knowledge within the context of the identified research gaps. First, the study adds to the limited literature on the impact of country governance on carbon emissions reduction, particularly with reference to scope 3 emissions. Second, with the sustainable development goals (SDGs) set to expire by 2030, the study provides empirical evidence on efforts governments of countries are making in achieving decarbonisation targets through improvement in country governance quality. Third, the study shows that the impact of the country governance on the carbon emissions performance of MNEs is contextual and varies across jurisdictions/geographical regions. Finally, the paper contributes to the debate on the actualisation of Agenda 2030, because presenting empirical evidence on the impact of country governance mechanisms on carbon emissions reduction—particularly scope 3 emissions—is an important discourse in the realisation of the SDGs. • Investigates the association between country governance and carbon emissions. • Sample of top multinational entities from 32 countries over 15 years. • Analysis of data using fixed OLS, 2SLS, propensity score matching, Heckman two-step. • Control of Corruption and Voice & Accountability curb carbon emissions. • Political Stability and Government Effectiveness encourage carbon emissions. [ABSTRACT FROM AUTHOR]