Environmental Attributes Research Articles

Are agrochemical‐free and biodiversity‐friendly attributes substitutes or complements? Evidence from a coffee choice experiment

AbstractEco‐labels inform consumers about the sustainable attributes of a product, but consumer face challenges to differentiate and select for specific attributes. Certification programs are similarly challenged to incentivize adoption of sustainable practices in product supply chains when consumer ability to differentiate sustainable attributes is low. This study investigates consumers' Willingness to Pay (WTP) for different environmental attributes of coffee production, and whether attributes that conserve biodiversity and limit agrochemical usage are substitutes or complements. We designed and implemented a hypothetical coffee choice experiment combining coffee farm vegetation management attributes that impact biodiversity (conventional monoculture, shade‐grown, or “Bird Friendly” systems that conserve wildlife habitat) and chemical input (conventional, pesticide‐free, or organic) attributes. We found that consumers think of biodiversity and agrochemical management attributes as sustainability substitutes and have the highest WTP for pesticide‐free and organic attributes. Consumer groups with strong concern about the future of the environment had high WTP for all environmental attributes, but still considered the attributes to be substitutes. Our results suggest that eco‐label programs with biodiversity and agrochemical attributes, such as the Smithsonian Bird Friendly® coffee certification, could increase market participation by simplifying environmental sustainability messaging, simplifying farm‐level certification requirements, and targeting environmentally concerned consumers. [EconLit Citations: D12, Q01, Q13, L66].

Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations

Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and reservoir management but is challenging to predict at large scales. It is unclear whether SSC is predictable using macroscopic environmental attributes and forcings. This study tested the feasibility of deep-network-based models to predict daily SSC at basin outlets given only basin-averaged forcings, readily-available physiographic attributes, and streamflow (from observation or model). We trained long short-term memory (LSTM) deep networks both separately for each of the 377 sites across the conterminous United States (CONUS) (termed “local models”), and on all the sites collectively (Whole-CONUS). The Whole-CONUS and local models presented median coefficient of determination (R2) values of 0.63 and 0.52, respectively. This comparison agrees with previously acknowledged “data synergy” effects for LSTM models, where more data from more sites can help improve predictions overall. Furthermore, the continental-scale analysis provided a wealth of insights about SSC patterns. Both local and Whole-CONUS models tended to be more successful where SSC-streamflow correlations (Rs-q) were high − typically in the humid Eastern US − and with lower SSC. Low Rs-q basins were often found in the arid Southwest with higher SSC. The highly-nonlinear SSC-streamflow relationships seem related to seasonality, basin size, and heterogeneity of land cover and rainfall within the basins, suggesting these basins need to be simulated at higher spatial resolution and may require additional inputs related to SSC induced by seasonality. The Whole-CONUS model also performed well for spatial extrapolation (basins not included in the training dataset, median R2 = 0.55), supporting large-scale modeling efforts. These state-of-the-art results using only minimal inputs suggest data-driven approaches can exploit the natural coevolution of sediment processes and the environment to support sediment modeling.

The impact of accounting environment, firm and loan attributes on non-performing loan ratios of countries: the moderating role of good governance

The impact of accounting environment, firm and loan attributes on non-performing loan ratios of countries: the moderating role of good governance

Vermicompost Supply Enhances Fragrant-Rice Yield by Improving Soil Fertility and Eukaryotic Microbial Community Composition under Environmental Stress Conditions.

Heavy-metal contamination in agricultural soil, particularly of cadmium (Cd), poses serious threats to soil biodiversity, rice production, and food safety. Soil microbes improve soil fertility by regulating soil organic matter production, plant nutrient accumulation, and pollutant transformation. Addressing the impact of Cd toxicity on soil fungal community composition, soil health, and rice yield is urgently required for sustainable rice production. Vermicompost (VC) is an organic fertilizer that alleviates the toxic effects of Cd on soil microbial biodiversity and functionality and improves crop productivity sustainably. In the present study, we examined the effects of different doses of VC (i.e., 0, 3, and 6 tons ha-1) and levels of Cd stress (i.e., 0 and 25 mg Cd kg-1) on soil biochemical attributes, soil fungal community composition, and fragrant-rice grain yield. The results showed that the Cd toxicity significantly reduced soil fertility, eukaryotic microbial community composition and rice grain yield. However, the VC addition alleviated the Cd toxicity and significantly improved the soil fungal community; additionally, it enhanced the relative abundance of Ascomycota, Chlorophyta, Ciliophora, Basidiomycota, and Glomeromycta in Cd-contaminated soils. Moreover, the VC addition enhanced the soil's chemical attributes, including soil pH, soil organic carbon (SOC), available nitrogen (AN), total nitrogen (TN), and microbial biomass C and N, compared to non-VC treated soil under Cd toxicity conditions. Similarly, the VC application significantly increased rice grain yield and decreased the Cd uptake in rice. One possible explanation for the reduced Cd uptake in plants is that VC amendments influence the soil's biological properties, which ultimately reduces soil Cd bioavailability and subsequently influences the Cd uptake and accumulation in rice plants. RDA analysis determined that the leading fungal species were highly related to soil environmental attributes and microbial biomass C and N production. However, the relative abundance levels of Ascomycota, Basidiomycota, and Glomeromycta were strongly associated with soil environmental variables. Thus, the outcomes of this study reveal that the use of VC in Cd-contaminated soils could be useful for sustainable rice production and safe utilization of Cd-polluted soil.

Consumers’ Purchase Intention toward Electric Vehicles from the Perspective of Perceived Green Value: An Empirical Survey from China

This study aims to expand the current understanding of the antecedents and consequences of green purchase intention within the context of perceived green value (PGV). This study uniquely combines PLS-SEM and NCA to provide a comprehensive analysis of the factors influencing green purchase intention, offering a novel methodological contribution to the field. By examining the roles of influence and interaction as key precursors to perceived responsibility and self-efficacy, the research adds new dimensions to understanding how these factors shape green purchase intentions. Additionally, we explore how PER and PSE impact green purchase intention (GPI), primarily through their mediating effects. The survey data collected from consumers reveal that PGV positively influences GPI. Furthermore, both PER and PSE exhibit significant chain mediation effects, with the mediation effect of perceived environmental responsibility being particularly strong. This study provides actionable insights for policymakers and electric vehicle manufacturers, emphasizing the importance of green education, certification systems, and the promotion of environmental and economic attributes to foster green purchasing behavior. The findings suggest that government efforts should focus on intensifying green education and promoting the establishment of certification and evaluation systems for electric vehicle products. Meanwhile, electric vehicle manufacturers should highlight the environmental and economic benefits of their products to stimulate green purchasing behavior.

Systematic Review of Built Environment Attributes of Walkability: Cases of Malaysia

Walkability has gained considerable traction in recent years as a key concept for promoting sustainable urban environments and healthier lifestyles. Built environment attributes such as infrastructure, streetscape design, land use mix, and street connectivity were found to have a high impact on walking behavior. However, due to the context-specific nature of walkability, where attributes deemed important in one context may not hold the same significance in another, this study aims to examine walkability attributes and methods within the Malaysian context. The goal is to offer valuable insights for researchers and policymakers operating in similar contexts. This study employed a systematic review following the PRISMA guidelines. A comprehensive search of SCOPUS and Google Scholar identified and assessed studies related to walkability in Malaysia. Rigorous screening and application of inclusion criteria yielded 32 Malaysian articles published between 2011 and 2021 for detailed review. Data extraction focused on specified factors including data source, built environment attributes, unit of analysis, and type of walking. The results identified both similarities and differences in the influence of built environment attributes on walking behavior in Malaysia compared to global patterns. Micro-scale factors, such as the aesthetics and attractiveness of walkways (65%), and safety and security (59%), were found to be leading factors in creating walking-friendly environments. Conversely, macro-level attributes, including density (15%) and mixed land use (18%), did not show a significant impact. This reliance on perceived assessments may struggle to capture the complexity of actual density and diversity, leading to inconsistent results. However, data collection and analysis approaches require further refinement. Future Malaysian research may prioritize the use of GIS-based walkability indexes to comprehensively measure walkability and improve the validity and accuracy of assessments. Additionally, consideration should be given to the regional transferability of these indexes.

Material energy citizenship through participation in citizen-financed photovoltaic projects

BackgroundCitizens are recognized as key actors in the energy system’s transformation by assuming novel roles beyond being mere energy consumers. Participation in renewable energy projects increases societal support and renders the decarbonization of the energy system more inclusive. Increasing numbers of citizen-financed photovoltaic (CiFi PV) projects exemplify this. Empirical studies on individuals who participate in CiFi PV, their perceived role(s), and their motivations, however, are scarce. This study addresses this gap through the lens of energy citizenship by analyzing individual participation.MethodsThe study surveyed CiFi PV participants across five projects in Switzerland (N = 510). After a comparison of the participants’ characteristics to the general public and a descriptive analysis of the perceived roles to participate, the study explores the individual motivations of participants. To that end, a motivational attributes scale, including finance, environment, local value creation, and symbolism, was adapted from a previous study. A hierarchical multiple linear regression was used to analyze which motivational attributes predict participants’ willingness to participate in future CiFi PV projects.ResultsWhile participants were primarily male, more affluent, better educated and politically more left-leaning than the average Swiss population, participants covered a wide range of sociodemographic characteristics and worldviews. Though CiFi PV is primarily marketed toward tenants, half of the participants were homeowners. Participants perceived themselves as energy citizens contributing to the energy transition and environmental preservation rather than as investors or energy producers. The regression analysis shows that motivations are relevant in explaining willingness to participate in future CiFi PV projects. We found that environmental, financial and local value creation-related motivational attributes are highly significant predictors, as well as slightly less significant symbolic attributes.ConclusionsThese results suggest that CiFi PV projects represent a material form of energy citizenship going beyond mere consumerism by enabling individuals to contribute to the energy transition. Given their capacity to engage diverse publics, policymakers should endorse projects emphasizing individual participation alongside non-commercial community-based models. This would require their integration into existing regulatory frameworks. Future energy citizenship studies should further explore how individuals perceive and conceptualize what it means to be an energy citizen.

Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It.

Honeybees (Apis mellifera L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.

ECOLOGICAL ELECTRIC CAR USABLE IN THE AGRICULTURAL SECTOR

Growing concern of carbon dioxide emissions, greenhouse effects and rapid depletion of fossil fuels raise the necessity to produce and adopt new eco-friendly sustainable alternatives to the internal combustion engine driven vehicles. For this reason, in the last decade, electric vehicles have become in some way widespread, principally because of their negligible flue gas emissions and lesser reliance on oil. Although electric cars were introduced many years ago, agriculture electric vehicles have recently gained attention. The world of agriculture has changed, small-specialized farm utility vehicles are now used for a specific purpose, and in several cases, they have an electric engine. In our research, we focused on the design and preparation of a prototype the variable, multifunctional ecological electric loader, which would be dimensionally adapted to the needs of use in greenhouse, interior growing halls, as a result of research by students of innovative dual technical education with effective support of their teachers. The result of our research was the design and construction of a prototype of a smaller electric loader, which meets demanding quality parameters (performance) and environmental attributes (reduced noise and almost zero emissions) for work in greenhouses.

Built Environment, Natural Environment, and Mental Health.

Mental health disorders have become a global problem, garnering considerable attention. However, the root causes of deteriorating mental health remain poorly understood, with existing literature predominantly concentrating on socioeconomic conditions and psychological factors. This study uses multi-linear and geographically weighted regressions (GWR) to examine the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that job sprawl and land mixed use are highly correlated with a lower risk of depression. Additionally, the presence of green spaces, especially in urban area, is associated with improved mental health. Conversely, higher concentrations of air pollutants, such as PM2.5 and CO, along with increased precipitation, are linked to elevated depression rates. When considering spatial correlation through GWR, the impact of population density and social capital on mental health displays substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals nuanced determinants. In northwestern counties, depression rates are more influenced by factors like precipitation and socioeconomic conditions, including unemployment and income segregation. In southeastern counties, population demographic characteristics, particularly racial composition, are associated with high depression prevalence, followed by built environment factors. Interestingly, job growth and crime rates only emerge as significant factors in the context of high depression risks in southeastern counties. This study underscores the robust linkages and spatial variations between built and natural environments and mental health, emphasizing the need for effective depression treatment to incorporate these multifaceted factors.

Exploring the effect of urban environment on cardiovascular health: a feasibility study using machine learning to predict heart rate variability responses to environmental attributes in CHF patients

Exploring the effect of urban environment on cardiovascular health: a feasibility study using machine learning to predict heart rate variability responses to environmental attributes in CHF patients

Volumetric pricing in rural Central America: Drivers of adoption and potential effects on water delivery

In rural and peri‑urban areas of Central America, community water organizations (CWOs) provide water to 60 % of the population, thereby playing a pivotal role in achieving multiple Sustainable Development Goals. However, the underlying environmental, climatic, and institutional factors explaining the adoption of volumetric pricing from these water providers and its effect on service delivery are typically overlooked in the literature. In this paper, we address two issues. First, we test whether volumetric pricing affects the service water delivery in a rural setting, drawing on a random sample of cross-sectional data on 154 CWOs in Costa Rica, Guatemala, and Nicaragua. We find that volumetric pricing is associated with substantially more successful water delivery, even when conditioned on institutional capacity, environmental attributes, climatic conditions, and country-fixed effects. Despite this strong relationship, volumetric pricing has yet to be widely adopted, particularly in Nicaragua and Guatemala. Therefore, as the second goal, we try to identify the institutional and socio-ecological conditions in which volumetric pricing is adopted. We find that volumetric pricing is more likely used when communities (1) experience adverse environmental and climatic conditions associated with water scarcity and (2) have greater institutional capacity. Our results highlight the importance of examining the social-ecological system to assess the performance and adoption of water management institutions

Application and Benefit Assessment of Sustainable Building Materials in High-Rise Building Design

With increasing environmental awareness, the use of sustainable building materials in modern high-rise architecture has become increasingly prominent. This paper provides a detailed analysis of Wood Chip Concrete Blocks (CBWC), exploring their application in high-rise building design, along with their environmental attributes, thermal performance, and structural integrity. Research indicates that CBWCs have a thermal resistance of R=5.26 m²K/W and fire resistance exceeding 180 minutes, significantly enhancing the building's energy efficiency and environmental friendliness. Simulation data reveals that the improved CBWC2 model, after partial filling and reinforcement, increased its peak horizontal force to 165.2 kN and reduced displacement to 9.99 mm, indicating enhanced structural stability. This study aims to use wood chip-reinforced concrete blocks to improve building energy efficiency and reduce carbon footprint while ensuring durability and safety, providing a sustainable material choice for future constructions.

Landscape classification with self-organizing map using user participation and environmental data: the case of the Seoul Metropolitan Area

This study aimed to develop a method for assessing landscapes using environmental data and user-generated data, which are commonly employed in landscape research. It focused on the Seoul metropolitan area in South Korea, devising evaluation indicators for five key concepts: naturalness, diversity, imageability, historicity, and disturbance. These indicators were used to assess the landscapes based on each index. We employed a self-organizing map, an artificial neural network technique, to categorize the landscape units and developed eight evaluation indicators for the five key concepts, organizing the study area’s landscapes into six distinct landscape units. This study identified landscape unit types with increased vulnerability to visual blight or heightened public awareness by considering both user characteristics and environmental attributes in the metropolitan area landscapes. Finally, we discussed future tasks for appropriate landscape management based on each landscape area’s characteristics to maintain and enhance landscape quality.

Risk Characterization of Firms with ESG Attributes Using a Supervised Machine Learning Method

We examine the risk–return tradeoff of a portfolio of firms that have tangible environmental, social, and governance (ESG) attributes. We introduce a new type of penalized regression using the Mahalanobis distance-based method and show its usefulness using our sample of ESG firms. Our results show that ESG companies are exposed to financial state variables that capture the changes in investment opportunities. However, we find that there is no economically significant difference between the risk-adjusted returns of various ESG-rating-based portfolios and that the risk associated with a poor ESG rating portfolio is not significantly different than that of a good ESG rating portfolio. Although investors require return compensation for holding ESG stocks, the fact that the risk of a poor ESG rating portfolio is comparable to that of a good ESG rating portfolio suggests risk dimensions that go beyond ESG attributes. We further show that the new covariance-adjusted penalized regression improves the out-of-sample cross-sectional predictions of the ESG portfolio’s expected returns. Overall, our approach is pragmatic and based on the ease of an empirical appeal.

Planning for Urban Sustainability through Residents’ Wellbeing: The Effects of Nature Interactions, Social Capital, and Socio-Demographic Factors

The COVID-19 pandemic provided a unique opportunity to examine how varied environmental and social factors in urban environments affect human wellbeing, which is an important dimension of urban sustainability. Past research has focused on individual dimensions of health and isolated nature interactions, limiting knowledge about how different environmental and social factors affect distinct aspects of residents’ wellbeing. Through quantitative analyses of social survey and environmental data in metropolitan Phoenix, Arizona (USA), we explored how distinct nature recreation activities—along with nature satisfaction and social capital—affected residents’ subjective, mental, and physical wellbeing across diverse neighborhoods during the COVID-19 pandemic, controlling for socio-demographics. Results reveal how distinct drivers shape different dimensions of wellbeing. Perceived social and environmental attributes of neighborhoods and proximity to nature preserves were associated with subjective wellbeing, while park visitation was linked to physical health. Nature interactions largely were unrelated to mental health. Changes in nature recreation during the COVID-19 pandemic did not significantly impact any dimension of wellbeing. Our research suggests that multiple local environmental and social features should be considered when designing healthy communities for urban sustainability.

Cultivating change in food consumption practices: The reception of the social representation of alternative proteins by consumers

This article is concerned with the dynamics of change in protein consumption practices from the perspective of the consumer. It is based on a model, informed by social representation theory, that aims to understand the role played by various types of representation of alternative proteins in the process of changing food consumption practices. It discusses the reception, by consumers, of the representations associated with alternative proteins on Instagram. Methodologically, three focus groups were organized with different consumer segments (omnivorous, flexitarian and vegetarian and vegan consumers), as well as seven individual interviews. Participants were submitted to the social representations of alternative proteins, and visual stimuli from social media were mobilized for this purpose. Results show that the publications which boast the environmental, animal welfare or health attributes of alternative proteins generally contribute to the cultivation of new elements of practices. While this kind of publications is essential to help consumers question their established practices linked to meat and dairy consumption, they can also generate a critical reception that is not conducive to change, making them a double-edge sword. Publications that relate to the representations involved in daily food consumption proteins (e.g. that alternative proteins are versatile and crowd-pleasing) emerge as being safer in terms of reception, although as standalone they may not be able to achieve a deep level of change in food consumption practices. The results of this study show the importance of deploying a diverse communication strategy about alternative proteins that appeal to a variety of consumer segments.

Exploring the Relationship between Built Environment Attributes and Physical Activity in Lower-Income Aging Adults: Preliminary Insights from a Multi-Level Trial.

The built environment has been linked to physical activity (PA) behaviors, yet there is limited knowledge of this association among lower-income midlife and older adults who are insufficiently active. The present cross-sectional study utilized baseline data collected between October 2017 and November 2019 from a clustered randomized controlled trial to determine how built environment attributes were associated with PA behaviors among midlife and older adults (n = 255) residing in or near affordable housing sites (n = 10). At each site, perceptions of the built environment were collected and scored at the participant level via the abbreviated Neighborhood Environment Walkability Survey (NEWS-A), while objective built environment attributes were measured and scored by trained research staff using the Physical Activity Resource Assessment (PARA). Multiple PA behaviors-walking, total PA, and moderate-to-vigorous PA (MVPA) (min/wk)-were measured using the validated Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire. Adjusted linear regression models examined associations between NEWS-A measures and PA behaviors, and site-level correlations between PARA measures and PA behaviors were examined using Spearman's rank correlations. At the participant level, adjusted models revealed that a one point increase in the NEWS-A aesthetics score was associated with a 57.37 min/wk increase in walking (β = 57.37 [95% CI: 20.84, 93.91], p = 0.002), with a similar association observed for street connectivity and MVPA (β = 24.31 min/wk [95% CI: 3.22, 45.41], p = 0.02). At the site level, MVPA was positively correlated with the quality of the features of local, PA-supportive environmental resources (ρ = 0.82, p = 0.004). Findings indicate that participant- and site-level measures of the built environment may play a role in promoting PA behavior among this demographic and similar populations. Results also suggest that improvements in aesthetic attributes and street connectivity, along with enhancing the quality of local, PA-supportive environmental resources, may be effective strategies for promoting physical activity among lower-income midlife and older adults.

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

Can green fiscal policy improve green total factor carbon efficiency? Evidence from China

Green fiscal policy has emerged as a crucial tool for promoting sustainable development in developing countries. The “National Comprehensive Demonstration City of Energy Conservation and Emission Reduction Fiscal Policy” (ECER fiscal policy) launched by the Chinese government in 2011 is a typical green fiscal policy, but its effect remains to be evaluated. This study measures the green total factor carbon efficiency (GTFCE) of Chinese cities from 2004 to 2019 and employs a time-varying difference-in-differences (DID) model to assess the causal effect of the ECER fiscal policy on GTFCE. The findings suggest that the ECER fiscal policy, combining financial incentives and environmental regulation attributes, can effectively enhance GTFCE in pilot cities. Mechanism analysis demonstrates that the low-carbon effect of the ECER fiscal policy is mainly through optimizing resource allocation and facilitating green innovation. Heterogeneity analysis further highlights the policy’s amplified impact in cities with officials under 54 years old and higher environmental attention from local governments. These findings offer valuable insights for policymakers in developing countries regarding fiscal policy optimization in advancing carbon reduction.