Green Index Research Articles

Analysis of the Correlation Between Factors Affecting Green Mining in Open-Pit Mines

Green mining is of great significance for the sustainable development of mines. However, the current green mine evaluation index system proposed in China is complex, and the intrinsic logical relationships between the indicators are not yet clear. To address this issue, this article conducts an in-depth analysis and study of the green mine evaluation index system. First, 16 indicators directly related to mining activities were selected. Then, using data mining technology based on the Apriori algorithm, the actual data of 30 open-pit mines were analyzed, and 7 effective association rules among the green mining indicators of open-pit mines were extracted. The results show that the association rule composed of ecological function requirements, mining technology, and advanced technology and equipment has the highest confidence level, with a confidence of 100% and a support of 68%. Advanced technology and equipment are identified as a key indicator for achieving green mining, playing a role in all 7 association rules. The remaining six effective association rules further emphasize the importance of the compatibility between different mining processes in achieving green mining goals, indicating that green mining is a complex system engineering requiring comprehensive decision making. The effective association rules proposed in this article provide a theoretical basis for mining enterprises to formulate more scientific work plans and thereby achieve green development goals.

Digital Economy, Green Innovation Efficiency, and New Quality Productive Forces: Empirical Evidence from Chinese Provincial Panel Data

As a new economic form driven and empowered by digital technology, the development of the digital economy has become a crucial pathway for boosting the advancement of new quality productive forces. This study employs the entropy method to measure the comprehensive evaluation index system for both the digital economy and new quality productive forces and further utilizes the unexpected output super-efficiency model to calculate the green innovation efficiency index. On this basis, panel data of 31 provinces in China from 2012 to 2022 are selected to conduct a quantitative analysis from a multidimensional perspective innovatively, examining the overall, heterogeneous, and spatial effects of the digital economy on new quality productive forces, as well as the mediating, moderated mediating, and threshold effects of green innovation efficiency. The research findings indicate that (1) the digital economy significantly promotes the development of new quality productive forces, and this conclusion is robust; (2) the impact of the digital economy on new quality productive forces exhibits regional heterogeneity; (3) there is a positive spatial spillover effect of the digital economy on new quality productive forces; (4) the digital economy can facilitate the development of new quality productive forces by enhancing green innovation efficiency; (5) industrial structure upgrading moderates the mediating effect of the digital economy on new quality productive forces, specifically regulating the promoting effect of green innovation efficiency on new quality productive forces; and (6) the impact of the digital economy on new quality productive forces exhibits a nonlinear characteristic of “increasing marginal effect”. Consequently, this study proposes targeted suggestions from four dimensions: accelerating the construction of digital infrastructure, vigorously enhancing green innovation efficiency, promoting the deep integration of digitalization and greenization, and facilitating industrial upgrading and coordinated development. These suggestions serve as valuable empirical insights into China’s transformation path selection and sustainable high-quality development in the new era.

Enhancing the visual environment of urban coastal roads through deep learning analysis of street-view images: A perspective of aesthetic and distinctiveness.

Urban waterfront areas, which are essential natural resources and highly perceived public areas in cities, play a crucial role in enhancing urban environment. This study integrates deep learning with human perception data sourced from street view images to study the relationship between visual landscape features and human perception of urban waterfront areas, employing linear regression and random forest models to predict human perception along urban coastal roads. Based on aesthetic and distinctiveness perception, urban coastal roads in Xiamen were classified into four types with different emphasis and priorities for improvement. The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. These findings offer crucial evidence regarding human perception of urban coastal roads, and can provide targeted recommendations for enhancing the visual environment of urban coastal road landscapes.

Construction and Application of Urban Green Space Ecosystem Service Assessment Indicator System and Assessment Method: A Case Study of Chifeng Central Urban Area, China

The assessment of urban green space ecosystem services is an important basis for urban ecosystem management and decision-making. To further enhance the accuracy of this evaluation, this paper refers to the main research results of international urban ecosystem service assessment and puts forward the main principles for the construction of an urban green space ecosystem service assessment index system. Based on this principle, the framework of the assessment indicator system was carried out by adopting the expert consultation method and frequency analysis method. This system includes the functions of leisure and recreation and the landscape premium in cultural services, air purification, noise abatement, carbon sequestration, oxygen release, and precipitation storage in regulatory services, and biodiversity conservation in supply services. The assessment methods for each functional category are chosen for their appropriateness, or the inadequacies of the existing methods are refined and improved. Then, we take the urban center of Chifeng City, China, as an example to carry out an empirical study on the urban green space ecosystem service assessment index system and assessment methods proposed in this paper. This study shows that the total value of green space ecosystem services in the downtown area of Chifeng City is CNY 229.91 million (Chinese Yuan). Among them, cultural services, regulating services, and supporting services accounted for 89.62%, 9.06%, and 1.32% of the total value, respectively. This study has important reference significance for the comprehensive quantitative assessment of the ecological service function value of urban green spaces, revealing the socio-economic value of urban green space resources while providing insights and references for the environmental assessment, planning, construction, and management decision-making of urban green spaces.

Ecofriendly spectrophotometric methods for simultaneous determination of remdesivir and moxifloxacin hydrochloride as co administered drugs in corona virus treatment

Remdesivir and moxifloxacin hydrochloride are among the most frequently co-administered drugs used for COVID-19 treatment. The current work aims to evaluate green spectrophotometric methodologies for estimating remdesivir and moxifloxacin hydrochloride in different matrices for the first time. The proposed approaches were absorbance subtraction, extended ratio subtraction and amplitude modulation methods. In order to determine the absorbance of the investigated medications in combination at the isoabsorptive point, the pure moxifloxacin hydrochloride absorbance factor is applied using the absorbance subtraction method, which modifies the zero absorption spectra of the drugs under investigation at the isoabsorptive point (229 nm). The spectrum of moxifloxacin hydrochloride is more extended in the plateau area between 340 and 400 nm, where remdesivir exhibits no absorption. So, also, the ratio spectra were successfully manipulated for quantification of the two drugs. Regarding the pharmacokinetic profile of remdesivir (Cmax 4420 ng/mL) and moxifloxacin hydrochloride (Cmax 3.56 µg/mL), the proposed methods were effectively used to spectrophotometrically determine remdesivir and moxifloxacin hydrochloride in plasma matrix. The new approach was validated using the ICH guidelines for specificity, linearity, precision, and accuracy. The greenness of the reported methodologies was evaluated using two metrics: the analytical eco-scale and the green analytical procedure index.

Regional green economies and Bitcoin's electricity consumption: Paving the way for global sustainability.

In the context of global sustainability goals, it is imperative to examine the interplay between emerging financial technologies and environmental objectives, which in turn offers insights into the implications of such technologies on sustainable investments and environmental policy decisions. Accordingly, this study investigates the influence of Bitcoin's energy consumption (BEC), as a representative of the cryptocurrency market, on the regional green economy indices of the United States, Europe, and Asia. Utilizing both unfiltered and filtered data, we conduct empirical analyses from both static and dynamic perspectives to account for the causal relationships that emerged during critical market developments, employing novel algorithms including forward recursive, rolling windows, and recursive rolling. The results reveal that energy-related developments play a dominant role in shaping the causal link between BEC and the green economy performance. More specifically, events such as the COVID-19 pandemic, the Russia-Ukraine war, and China's ban on cryptocurrency mining appear to significantly drive casual effects. After filtering out the impacts of global equity markets and information technology developments, we observe even more pronounced impacts on these causal relationships. Our findings highlight the need for coordinated global actions to address the energy consumption of the cryptocurrency market, facilitating the transition to global sustainability.

Stochastic behavior of green bond premiums

This paper aims to examine the stochastic behavior of green bond premiums that can characterize the benefits of green bonds. We propose a novel affine model of green bond pricing with mean-reverting interest rates and green bond premiums and a new model parameter estimation method using conventional and green bond prices to capture the stochastic behavior. Then, the model parameter estimation results demonstrate mean-reverting stochastic behavior for conventional bond yield and green bond premium using the US and EU green bond indexes for Corporate and three corporate bond indexes for intermediate, total, and long-term periods of the Bloomberg Fixed Income Indices from November 3, 2014 to December 11, 2020. Comparative statics using simulated green bond premiums show that green bond premiums orient toward negative values in nature. Moreover, the stochastic behavior of green bond premiums demonstrates that the greenness of green bonds has a downward effect on interest rates in COVID-19 and has a mitigating impact on liquidity risk in corporate bond markets. These results confirm the benefits of green bonds. Finally, the discussions secure the validity of the green bond pricing model by conducting econometric analyses of regime switching, principal component ones, and the GARCH (1,1) model.

Tail risk contagion and multiscale spillovers in the green finance index and large US technology stocks

Tail risk contagion and multiscale spillovers in the green finance index and large US technology stocks

Study on the Impact of Green Finance and Environmental Regulations on Haze Pollution in China

With the rapid economic growth in China, the years from 2012 to 2017 witnessed a severe outbreak of haze pollution. In response, numerous enterprises were shut down or forced to reduce production, leading to significant negative impacts on the economy and society. Consequently, the scientific management of haze pollution has become a focal point of China’s environmental research. This paper explores the influence of green finance and environmental regulations on haze pollution, employing spatial autoregressive and spatial Durbin models to conduct an empirical analysis based on panel data from 30 Chinese provinces spanning 2000 to 2017. The following conclusions were thus drawn: from 2010 to 2017, the green finance index of the Beijing–Tianjin–Hebei region and Liaoning Province, which suffered severe haze pollution, showed an overall downward trend; efforts to strengthen environmental regulations played an important role in reducing air pollution, especially the PM2.5 level; the improvement in the green finance level can help reduce haze pollution; and green finance has an obvious spatial spillover effect. A number of policy recommendations were proposed on this basis, including strengthening inter-regional cooperation and exchanges in green finance, promoting the scientific application of environmental regulations in areas with serious haze pollution, strengthening the implementation of green finance strategies in areas with serious haze pollution, and establishing regional green finance demonstration zones. The purpose of this study is to provide meaningful support for China’s scientific management of haze pollution through an analysis of the impact of green finance and policy optimization. The innovative aspects of this research lie in its pioneering use of spatial econometric models to investigate the role of green finance and environmental regulations in controlling haze pollution. The findings reveal that advancements in green finance can significantly alleviate haze pollution.

Portfolio Diversification and Dynamic Interactions between Clean and Dirty Energy Assets

Clean energy, with its focus on environmental sustainability and efficiency, has gained significance as concerns over the impact of traditional energy growth. However, there is limited evidence on the value of clean energy investments. This paper explores the role of clean energy in a balanced investment portfolio by examining two traditional energy assets (crude oil and natural gas) and two clean energy assets (SPDR S&P Kensho Clean Power ETF and iShares Global Clean Energy ETF). Using a time-varying parameter vector autoregression (TVP-VAR) model on daily data from October 2021 to January 2024, we analyze the evolving connectedness between these assets. Our results highlight dynamic interactions, with green finance indices like CNRG acting as net shock transmitters, while traditional energy indices, such as WTI and gas, primarily receive shocks. The analysis suggests that green assets, particularly ICLN, enhance portfolio stability and hedging efficiency, especially in minimum correlation and risk parity portfolios. Fossil fuels, especially gas, exhibit higher volatility, requiring careful portfolio management. Ultimately, integrating ESG criteria and adapting investment strategies to market conditions may enhance responsible investing and long-term value creation.

Green development in the Chengdu-Chongqing economic zone: evolution and drivers from a “production-living-ecology” perspective

Green development is key to promoting regional sustainable development. We construct an evaluation index system for green development levels based on the “sansheng” dimensions—production, living, and ecology. We rely on the “sansheng” (production, living, and ecology) dimensions, combined with the analytic hierarchy process (AHP) and the entropy weight method to analyze indicator weights, to construct an evaluation index system for green development levels. This system enables the identification of the evolution patterns of green development and the analysis of driving factors in the Chengdu-Chongqing economic zone from the “sansheng” perspective. The results indicate that: (1) The green development level in the Chengdu-Chongqing Economic Zone has been continuously rising, with the average index increasing from 0.197 to 0.254. Yuzhong District and Chengdu City have shown particularly high green development levels; in 2020, the green development level index for Yuzhong District reached 0.568, while Chengdu City’s index reached 0.522. (2) The spatial clustering of green development levels in the Chengdu-Chongqing economic zone exhibited a trend of first strengthening and then weakening, with the highest clustering degree observed in 2015. (3) National strategies have significantly promoted the improvement of regional green development levels. The average green development index during the pre-establishment, initial development, and rapid development stages of the Chengdu-Chongqing Economic Zone increased from 0.205 to 0.229, and then to 0.254. (4) The Theil index results show an increase in the disparity of green development levels among different regions within the Chengdu-Chongqing Economic Zone.The results of the optimal scaling regression model show that the driving factors with a significant impact on the level of green development include the Number of physicians per million people, Public library book collections Per 100 people, Per capita regional GDP, and Number of secondary schools Per million people, each contributing over 15% to the impact. These findings provide valuable data support for formulating regional economic development strategies and are conducive to advancing sustainable development.

Heterogeneous effects of agricultural insurance on agricultural green development: evidence from Sub national economies of India

The current paper aims to assess the relative competitiveness of agricultural green development across 27 Indian states and explore the factors influencing this development. Given India's significant role as a global producer of various crops such as rice, wheat, sugarcane, cotton, and pulses, and with approximately 43.96 percent of the labour force engaged in agriculture, the sector's performance is crucial for achieving Sustainable Development Goals (SDGs). The green development index has been created through the entropy method by using seven variables, where Uttar Pradesh and Telangana demonstrate higher green competitiveness. Conversely, states like Himachal Pradesh, Uttarakhand, and Punjab exhibit lower levels of green competitiveness due to their elevated consumption of fertilizers and pesticides. To understand the determinants of agricultural green development competitiveness, Quantile regression has been employed. The findings highlight a positive relationship between (the endogenous variable) and Per capita income, Bank branches in rural areas, hectares of land insured and capital expenditure on agriculture whereas increased cropping intensity and power availability per capita has negative relationships. Furthermore, employing the Dumitrescu and Hurlin (2012) panel Granger causality test reveals that economic growth at the state level in India precedes and influences agricultural green development. This suggests a dynamic relationship wherein economic progress fosters advancements in agricultural sustainability.

Beyond green: Unveiling the impact of urban park quality and greenery on children’s physical activity

Urban green spaces facilitate children’s physical activities by providing opportunities for engagement with natural settings. However, there is a lack of empirical research examining the nexus between the greenery features and spatial quality in urban parks and park-based physical activities among children. Moreover, the influence of children’s perceptions of their environment has been overlooked. Through the application of remote sensing and field surveys, we evaluated the greening and quality of 34 play spaces within 20 urban parks, employing on-site measurements and unmanned aerial vehicle observations to identify and quantify the intensity and density of children’s physical activities. Utilizing Hierarchical Linear Modeling and Simple Mediating Modeling, we investigated the mediating effects of green spaces on physical activity and perceived environmental qualities. Our findings indicate a curvilinear (inverted U-shaped) relationship between urban park greening metrics, such as vegetation diversity and green view index, and the dosage of children’s physical activity. The interplay between the quality of space and greening level in shaping physical activity demonstrates complex dynamics of synergy and competition. The greening level of play spaces positively impacts the intensity and density of children’s physical activities and is partly moderated by perceived environmental factors, notably safety and attractiveness. These insights contribute empirical evidence and decision-making guidance for urban greening and the development of child-friendly urban spaces.

TLC densitometric approach for concurrent determination of quinary mixture for treatment of migraine with appraisal to method greenness and whiteness

A new accurate, precise thin layer chromatographic (TLC densitometric) approach was developed for the simultaneous analysis of ergotamine tartrate (ERG), phenobarbital sodium (PHEN), caffeine anhydrous (CAF), dipyrone sodium (DIP) and meprobamate (MEP) in their pure form and in their combined formulation; (MIGRANIL tablets). TLC separation depended on using a stationary phase of TLC plates F254 (20 × 10 cm) and using a developing system of ethyl acetate: methanol: n- hexane (8:2:3, by volume) with UV scanning at 254 nm for ERG, PHEN, CAF and DIP. For MEP, detection was done at 560 nm after spraying with 0.3% ethanolic solution of para -dimethylaminobenzaldehyde (PDAB) and heating at 110 ºC for 30 min. Analysis was performed in the concentration intervals of 0.06-2, 0.15–5 µg/band for ERG and PHEN, respectively; while for CAF, DIP and MEP it was done in the ranges of 0.3-9, 0.5–10, 1–100 µg/band, respectively. Moreover, the method was used for the quantitation of the five studied drugs in their unique formulation and the recovery% was calculated and found to be 102.04, 100.73, 100.65, 99.91, 1nd 101.77% for ERG, PHEN, CAF, DIP, and MEP, in order and all these outcomes proved excellent selectivity of the method. Other validation parameters were tested following ICH guidelines. Accuracy ranged from 98.02 to 100.53% while the obtained %RSD of interday day precision ranged from 1.215 to 1.940%. The environmental impact and performance of the method was tested with several tools; Analytical eco-scale, green analytical procedure index (GAPI), Analytical GREEnness Metric Approach (AGREE), Blue applicability grade index (BAGI), and RGB additive color model. Results ensured minimum hazardous effect on health and environment. This is the first analytical approach developed for simultaneous analysis of the quinary mixture which can be easily applied in any analytical laboratory.

Exploring the Relationship Between Visual Perception of the Urban Riverfront Core Landscape Area and the Vitality of Riverfront Road: A Case Study of Guangzhou

The vitality of riverfront districts, as a crucial component of urban livability, is profoundly influenced by human visual perception of the surrounding environment. This study takes the Pearl River in Guangzhou as an example and explores the relationship between the visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. Employing subjective environment perception prediction methods and analyzing the riverfront landscape pictures captured by the research team, we quantified six essential perceptual dimensions. Furthermore, we evaluated the vitality of Riverfront Road through a four-step process: 1. measuring key visual indices of Riverfront Road, including the green view index (GVI), water view index (WVI), sky view index (SVI), and building view index (BVI); 2. evaluating the proximity of cultural landmarks to Riverfront Road; 3. calculating the convenience of driving, buses, and subways for Riverfront Road with the network analysis method; 4. deriving the vitality value of Riverfront Road through the combination of hotspot data from Baidu. With the application of random forest and result comparisons, we obtained a comprehensive analysis of the correlation between visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. The results reveal the significant correlation between these two factors and highlight that visual perception of the old city landscape area is superior to that of the new city, although the cultural landmarks and transportation convenience play essential roles in the improvement of vitality in Riverfront Road. It is evident that relying solely on visual design may fail to prominently boost vitality. Overall, spatial design should adopt a multidimensional approach, integrating various factors such as transportation convenience, social interaction venues, cultural activities, etc., to create a cohesive vitality network.

Eco-Friendly Capillary Electrophoresis Method for the Quantification of Apixaban in Oral Dosage Form

The present work describes, for the first time, an eco-friendly capillary zone electrophoresis (CZE) methodology for determining apixaban (APX), an oral anticoagulant. Separation was carried out using a fused silica capillary with an applied voltage of 20 kV and hydrodynamic injection for 5 s at 50 mbar. The electrolyte system consisted of 50 mM sodium borate at pH 8.0. The method was validated as recommended by ICH, establishing selectivity, linearity, precision, accuracy, and robustness. The analytical procedure was linear in the range of 10–125 µg mL−1 (r = 0.999). The limits of detection and quantification were determined to be 1.77 µg mL−1 and 5.37 µg mL−1, respectively. Precision was evaluated by relative standard deviation (RSD), with an intra-day RSD (n = 6) of 0.96% and an inter-day RSD (n = 18) of 4.42%. The average recovery rates for the pharmaceutical samples were excellent, ranging from 98.03% to 101.01%, with an RSD of 1.51%. Moreover, the green profile of the method was evaluated using the National Environmental Methods Index (NEMI), Analytical Eco-Scale, Green Analytical Procedure Index (GAPI), and Analytical GREEnness Metric (AGREE) assessment tools. The results suggest that the CZE method aligns with the principles of green chemistry, offering a greener alternative for the routine analysis of APX quality control. Also, the method presents a low-cost analysis, using fewer organic solvents and minimizing waste generation compared to techniques commonly used in routine analysis.

Satellite-Based and Street-View Green Space and Adiposity in US Children

Green space may be associated with obesity outcomes in children by promoting physical activity and reducing stress. However, previous studies have mostly used a single measure of green space and body mass index (BMI) as the sole indicator of adiposity. To examine associations of the satellite-based normalized difference vegetation index (NDVI) and street-view imagery-based green space metrics with comprehensive adiposity measures in children. The Project Viva cohort study observed participants from mid-childhood (mean [SD] age, 8.0 [0.8] years) and early adolescence (mean [SD] age, 13.3 [1.0] years) through late adolescence (mean [SD] age, 17.8 [0.7] years) between January 1, 2007, and December 31, 2021. This analysis included children with data on green space and adiposity measures. Data were analyzed from January 1 to June 30, 2024. Green space metrics (ie, percentage of trees, grass, and other green space) using street-view imagery and deep learning algorithms along with the NDVI were linked with participants' home addresses during mid-childhood and early adolescence. Body mass index (calculated as weight in kilograms divided by height in meters squared) and waist circumference z scores were calculated from anthropometric measurements during early and late adolescence. The total fat mass index (FMI; calculated as fat mass in kilograms divided by height in meters squared) and trunk FMI were obtained using dual-energy x-ray absorptiometry. Separate linear regression models adjusted for a child's age, sex, individual and neighborhood socioeconomic status, and population density estimated prospective associations of the NDVI and street-view green space (in mid-childhood or early adolescence) with 4 adiposity measures (in early or late adolescence). The sample included 843 children (mean [SD] age at mid-childhood, 7.9 [0.8] years; 423 girls [50.2%]) at baseline. After adjustment for confounders, a higher NDVI (per 1-SD increase) within 270 m in early adolescence was associated with lower z scores for BMI (-0.16 [95% CI, -0.27 to -0.04]) and waist circumference (-0.13 [95% CI, -0.23 to -0.03]), as well as a lower total FMI (-0.62 [95% CI, -1.02 to -0.23]) and trunk FMI (-0.33 [95% CI, -0.55 to -0.12]) in late adolescence. Higher percentages of street-view other green space (flowers, plants, and fields), but not street-view trees and grass, within 500 m in early adolescence were associated with lower BMI z scores (-0.09 [95% CI, -0.18 to -0.01) in late adolescence. The NDVI and street-view green space exposure at mid-childhood were not associated with adiposity measures in early adolescence. This cohort study of US children suggests that higher levels of satellite-based NDVI greenness and percentages of street-level green space components (flowers, plants, and fields) were associated with lower adiposity. The findings support the exploration of increasing residential green space levels and adding specific green space components as an urban planning and public health intervention strategy to combat the prevalence of childhood obesity in the US.

Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning

Urban greening plays a crucial role in maintaining environmental sustainability and enhancing people’s well-being. However, limited by the shortcomings of traditional methods, studying the heterogeneity and nonlinearity between environmental factors and green view index (GVI) still faces many challenges. To address the concerns of nonlinearity, spatial heterogeneity, and interpretability, an interpretable spatial machine learning framework incorporating the Geographically Weighted Random Forest (GWRF) model and the SHapley Additive exPlanation (Shap) model is proposed in this paper. In this paper, we combine multi-source big data, such as Baidu Street View data and remote sensing images, and utilize semantic segmentation models and geographic data processing techniques to study the global and local interpretation of the Beijing region with GVI as the key indicator. Our research results show that: (1) Within the Sixth Ring Road of Beijing, GVI shows significant spatial clustering phenomenon and positive correlation linkage, and at the same time exhibits significant spatial differences; (2) Among many environmental variables, the increase of green coverage rate has the most significant positive effect on GVI, while the increase of building density shows a strong negative correlation with GVI; (3) The performance of the GWRF model in predicting GVI is excellent and far exceeds that of comparison models.; (4) Whether it is the green coverage rate, urban built environment or socioeconomic factors, their influence on GVI shows non-linear characteristics and a certain threshold effect. With the help of these non-linear influences and explicit threshold effects, quantitative analyses of greening are provided, which can help to assist urban planners in making more scientific and rational decisions when allocating greening resources.

A Modified Green Star Area (MoGSA) and software to assess greenness of reactions in the chemistry laboratories.

The environmental and health impacts of chemical processes have been a growing concern, leading to the establishment of Green Chemistry principles. Introducing new metrics for the assessment of methods' greenness is crucial to evaluate the exerted efforts to conserve the environment. In this work, we introduce a Modified Green Star Area (MoGSA) and software to assess the greenness of chemical reactions in laboratory settings. MoGSA refines the traditional Green Star Area Index (GSAI) by allowing users to selectively apply specific principles of Green Chemistry based on their relevance to the chemical process being evaluated. This approach addresses the limitations of GSAI, which often lacks clear boundaries between green and non-green practices and does not account for the varying applicability of the 12 Green Chemistry principles across different contexts. Through comparative case studies on catalytic stereoselective reduction of acetophenone, MoGSA demonstrates its utility in providing a more refined and flexible assessment, enhancing both educational and industrial applications of sustainable chemical practices. The software is available as an open source at https://bit.ly/MOGSA.

Green finance innovation and carbon neutrality strategies: sustainable development pathways and policy frameworks

IntroductionThis paper analyzes the influence of green finance innovation on the green innovation ability of Shanxi Huangtupo Coal Industry Group Co., Ltd.MethodsBy constructing a fixed-effect model. This study uses the number of green patent applications by enterprises as indicator to measure green innovation, with the green finance development index as a key explanatory variable, while controlling for other factors such as asset-liability ratio and listing time.ResultsThe results show that there is a significant positive correlation between green finance innovation and the green innovation of enterprises, indicating that the development of green finance has effectively promoted the green technology innovation in enterprises, which helps to achieve carbon neutrality.DiscussionThe study provides a reference for policymakers to promote the development of lowcarbon economy through financial policies. Linking green finance innovation with the green innovation capabilities of specific high emission sector enterprises provides new insights into the role of green finance in promoting enterprise carbon neutrality.