Sustainable Development Decisions Research Articles

Optimizing ESG reporting: Innovating with E-BERT models in nature language processing

Developing a quantitative Environmental, Social, and Governance (ESG) rating tool using technology to reduce manpower requirements and ensure the objectivity and authenticity of the ratings is an important research question in the ESG field. The primary objective of this research is to develop an automated system, encapsulated in the Bidirectional Encoder Representation from Transformers for ESG rating (E-BERT) model, that evaluates ESG information with a high degree of objectivity and consistently delivers reliable rating outcomes. The E-BERT model, an advanced natural language processing (NLP)-based tool, is designed to transform ESG reporting by automating the evaluation process. Utilizing the architecture of Google’s BERT, this model offers precise and consistent ESG ratings, focusing on accurately assessing the sustainability contributions of enterprises. This research is initiated due to the absence of a comprehensive ESG dataset and involves constructing a tailored corpus that supports the E-BERT model. E-BERT meets the demand for objective ESG assessments by filtering out irrelevant data and standardizing criteria across various sectors, thereby streamlining the process and minimizing the need for manual intervention. This model not only automates the rating process but also achieves a notable 93% accuracy rate. By enhancing transparency and reducing biases, E-BERT marks a substantial improvement in ESG reporting, offering a robust tool for stakeholders to reliably assess corporate ESG performance. The success of the E-BERT model confirms its potential as a pivotal resource in supporting informed decision-making for sustainable development.

Dynamic analysis of landscape drivers in the thermal environment of Guanzhong plain urban agglomeration

Climate change caused by rapid urbanization in the Guanzhong region of China is becoming an increasingly significant problem. Previous empirical studies have confirmed that landscape patterns inextricably linked with the thermal environment, but static results based on a single temporal cross section of image data provide only a partial understanding. In this paper, we constructed a dynamic framework using Weather Research and Forecasting Model (WRF) for temperature simulation and Geodetector to study the landscape factors and their interactions that influence near-surface temperature (NST) changes in the Guanzhong Plain Urban Agglomeration (GPUA) between 2000 and 2020. Results showed that the GPUA average NST increased by 0.012 °C and 0.053 °C in January and July from 2000 to 2020, respectively. In terms of the dynamic correlation between landscape patterns and NST, cropland (CPL) was negative, urban land (UBL) was positive, and the remainder of the landscapes differed in winter and summer. Furthermore, results from the Geodetector showed that UBL embodied a stronger influence in summer than during winter months. This finding helps to explain why the average NST increase is higher in summer than during winter. The Dynamic Q values (DQ) of the area-based landscape metrics were generally larger than those of other spatial configuration metrics, and the interaction results showed that the landscape metrics of various land-cover classifications were enhanced, indicating that the superposition effect among landscape metrics needs to be taken into account in landscape planning in addition to area factors. The study of the relationship between landscape patterns and thermal environment considering dynamic perspective using WRF offers an important theoretical reference allied with practical guidance for understanding and adapting to forthcoming change in our climate through which we can help drive sustainable development decisions of the GPUA.

Technical feasibility factors affecting urban environmental sustainability in decision-making: Erbil-Iraq

This paper introduce decision-making model of sustainable urban development in Erbil city in Iraq. The decision-making model will includes higher-level policy orientation, grass-roots environmental protection, economic feasibility, and environmental pressure. The quantitative analyze of urban environmental sustainable development decision-making in prefecture-level. The data analysis collected from the annual government work report using seven specific factor, which set up according to the theoretical model framework. The results show that the driving force of sustainable development is mainly from top to bottom, the macro strategy of the central government and other upper-level governments is the core factor of sustainable transformation, and the demand for sustainable development of the grassroots people needs to continue to increase. In addition, prominent environmental issues have attracted the attention and actions of local governments, but urban governments still regard sustainable development as a distributive policy to a large extent, and the awareness or conditions for seeking economic welfare through environmental governance have not yet achieved.

Innovation in the territorial impact assessment: an application to large infrastructure projects of the Italian recovery and resilience facility

ABSTRACT The Territorial Impact Assessment (TIA) has evolved significantly over the past two decades, becoming a pivotal tool in shaping European public policy. Initially, TIA aimed to tailor investments to the specific needs of various territories, ensuring allignement with broader EU and national objectives. The assessment of European Cohesion Policy 2014–20 highligthed the TIA’s utility in guiding sustainable development decisions. The integration of TIA into the Recovery and Resilience Facility (RRF) marks a notable shift, emphasising sustainability in policy planning and the allocation of funds. This paper outlined how TIA can be embedded within the RRF’s planning and evaluation mechanisms, using Italian large infrastructure projects as an example. The incorporation of TIA into a multifaceted evaluation framework, which encompasses both economic-financial and sustainability considerations utilising the Sustainable Environmental/Economic Management Approach (STeMA) model, demonstrates its effective integration. This approach balances economic viability with environmental and social sustainability aligning with the EU Green Deal pillars and the Territorial Agenda 2030. It also ensures adherence to economic and financial considerations coming from cost-benefit analysis, while also upholding sustainability under the ‘do no significant harm’ principle. This match thus could foster a more resilient and sustainable future for EU territories.

Effect of Land Use Land Cover on the Hydrology of Rwizi River Catchment located in Southwestern Uganda

Changes in land use and land cover usually affect the hydrological processes of river flows, sediment transport, and water quality on a global scale. Hence, the objective of this paper was to investigate the effect of Land Use Land Cover (LULCC) on the Hydrology of Rwizi River Catchment located in South-western Uganda using appropriate standard techniques and procedures including Soil and Water Assessment Tool (SWAT). Model calibration and validation demonstrated good agreement (R² = 0.76-0.86, NSE = 0.75-0.78). LULC analysis revealed significant increases in built-up areas and forestland, with declines in grazing land and wetlands. These changes were driven by socio-economic factors and hydro-climatic influences. The study found strong correlations between LULC types and sediment yield, highlighting the implications for water quality and erosion control. The results show that urban growth raises surface runoff and peak flows as a result of a rise in impervious surfaces, whereas agricultural intensification raises water demand and lowers base flow, especially during dry spells. Deforestation has led to increased sedimentation rates, degrading water quality. These findings underscore the significant anthropogenic and climatic impacts on LULCC dynamics and hydrological processes in the catchment. The study emphasizes the critical need for integrated watershed management strategies to mitigate these impacts and ensure sustainable water management practices. By advancing understanding in hydrological sciences, this research informs policy and management decisions for sustainable development in tropical river basins globally.

Perceived ecosystem services differ substantially from calculated services using biophysical models

ContextEcosystem services are fundamental for the well-being of residents and are an important basis for making regional sustainable development decisions. Existing studies mainly capture ecosystem services perceived by residents through questionnaires, or quantify the provision of regional ecosystem services through economic evaluation and biophysical evaluation. However, the discrepancy between model calculations and residents' perceptions remains lacking.ObjectivesRapidly urbanizing watersheds can serve as a prime example for exploring the differences in ecosystem service outcomes between the two perspectives.MethodsIn this study, we quantified the potential supply of nine ecosystem services and residents' perceptions of these services in the Guanting Reservoir basin through biophysical modelling and questionnaire surveys. We then analyzed the discrepancies between the two perspectives through the Wilcoxon signed-rank test.ResultsThe results showed that among the vast majority of the population, half of the nine ecosystem services exhibited significant differences in comparisons between perceived values and model-calculated ones. The differences were more prominent for regulating and supporting services among urban residents, while for provisioning and cultural services among rural residents.Conclusions These differences highlight the necessity to consider quantitative results from diverse perspectives as well as on the benefits of different groups of interest. In rapidly urbanizing watersheds, infrastructure should be reinforced in rural areas to enhance rural residents’ accessibility of ecosystem services. With regard to urban areas, particular attention should be directed towards changes in regulating and supporting services.

Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina

Abstract The research subject is the application of geographic information systems (GIS) in assessing land suitability for agriculture in the Sana River Basin in Bosnia and Herzegovina. The aim of the research is to apply the analytic hierarchy process (AHP) in identifying suitable areas for agricultural production. Within the AHP framework, the following factors were considered: pedology, land use, elevation, slope, aridity index, and distance from rivers. The results of the suitability assessment underwent reclassification (RP) and cluster analysis processes (CAPs). It was found that very unsuitable land (1) covers an area of 0.15% (RP) or 5.83% (CAP), unsuitable land (2) covers 3.44% (RP) or 17.52% (CAP), conditionally suitable land (3) covers 32.11% or 28.47% (CAP), suitable land (4) covers 56.29% or 28.57% (CAP), and very suitable land (5) covers 7.98% (RP) or 19.59% (CAP). At the study area level, a supervised classification process was conducted to identify land use classes: meadows/pastures, water, forest, agricultural, and built-up areas. RP and CAP results were overlaid with supervised classification results to determine the amount of land used for agricultural purposes within each suitability class. It was determined that currently, for agricultural purposes, 0.04 km2 (RP) or 0.88 km2 (CAP) of very unsuitable land (1) is used, 0.41 km2 (RP) or 7.28 km2 (CAP) of unsuitable land (2), 15.75 km2 (RP) or 27.52 km2 (CAP) of conditionally suitable land (3), 185.15 km2 (RP) or 107.06 km2 (CAP) of suitable land (4), and 42.99 km2 (RP) or 101.65 km2 (CAP) of very suitable land (5). The research findings hold substantial importance in elucidating both the potential and constraints of land use practices as a vital natural resource within agriculture. They also have practical importance for relevant institutions in terms of agricultural sector development and making timely land use planning decisions for sustainable development.

Nexus between country governance, globalization, sustainable development policy, fair trade and economic sustainability: a mediation-moderation approach

Sustainable development of the economy is the goal of every country in the world. The eco¬nomic growth of China is accepted worldwide, but the Chinese economy also suffers from an economic crisis. The role of the Chinese government is important for chinses businesses and the economic sus¬tainability of the country. The theoretical framework of this study is designed to determine the impact of globalization and sustainable development policy on fair trade and economic sustainability in China. A questionnaire based on Likert scale were employed with a random sampling technique to collect the cross-sectional data for this research and 1094 responses were collected at the rate of 55%. AMOS sta-tistics tool was employed for the findings of this study based on a research questionnaire. The research concludes that globalization and sustainable development policy have a significant impact on fair trade and economic sustainability in China. This study introduced a new framework related to economic sus¬tainability in the body of knowledge. The theoretical and practical implications of this study have signif¬icance for literature and practitioners in China to achieve sustainability in the economy. The research is novel as it highlighted the importance of globalization and sustainable development policy for fair trade and economic development in China. Accordingly, it would be helpful for the policy makers to have reli¬able decisions for sustainable development and promotion of fair trade.

Future land use/land cover and its impacts on ecosystem services: case of Aydın, Turkey

AbstractThis paper aims to reveal the impacts of land use/land cover changes on ecosystem services and to guide sustainable development decisions by modelling the future land use/land cover pattern and its ecosystem services in the case of Aydın province, Turkey. In this context, the study examined land use/land cover changes in Aydın province, predicted future land use/land cover patterns with three scenarios (natural development, ecosystem service-based development, and economy-based development) using the PLUS model, and determined the impact of land use/land cover changes on InVEST generated carbon storage and habitat quality ecosystem services. The key drivers of land use/land cover change in Aydın were agricultural expansion, the transformation of different vegetation types into each other, and, even on a small scale, urbanization. The study revealed that changes in the spatial pattern of land use/land cover types, together with the effect of changing vegetation patterns, have a significant impact on carbon storage and habitat quality. While reductions in shrubland and forests were detrimental, transformations from openspaces to them, particularly at their boundaries, enhanced the capacity of carbon storage and habitat quality. On the other hand, even though the scenarios of ecosystem service-based development and economy-based development were based on the economic value of ecosystem services and the value of products/services obtained from different land use/land cover types, respectively, the ecosystem service-based development scenario was characterized by more effective land use/land cover management in terms of maximizing both the economic and ecological benefits. This highlights the significance and emerging need for prioritizing ecological considerations in urban planning.

Identification of Urban Expansion Driving Factors using CA-Markov model: a Case Study Demak and Jepara Regency, Indonesia

Land cover changes are a critical concern in a developing country where rapid urbanization and population growth intersect with environmental dynamics. Understanding the driving forces behind these changes is essential for sustainable development and effective land management. This study analyzes land cover changes in Demak-Jepara regency, Indonesia, over a 20-year period using Landsat data. The objective is to identify the dominant factors driving the increase in built-up areas, considering both natural and human-induced factors. Factors such as road distance, existing build area, and natural features are evaluated. Using ArcGIS and Idrisi Selva's Land Change Modeler, land cover data is processed, and Cellular Automata-Markov analysis is conducted. The study considers a cell size of 30 x 30 meters and a time step of 5 years from 2001 to 2009. Transition persistence analysis identifies significant factors, validated using 2020 land cover data through AUC (Area Under the Curve) and ROC (Relative Operating Characteristic) analysis. The combination of natural and human-induced factors (scenario-C) shows the highest AUC value of 0.9406, indicating better conformity with 2020 land cover. Dominant factors in scenario C include roads, existing built-up areas, river order, and slope gradient. Results reveal that road development and proximity to existing settlements are the primary drivers of land cover changes. Natural factors like river order and slope gradient have a lesser impact, while the coastline has minimal influence. These findings highlight the importance of considering both natural and human-induced factors in land use planning. They provide valuable insights for policymakers and land managers in making informed decisions for sustainable development and land use strategies. Keywords: Land Cover Changes; Driving Factors; Natural Factors; CA-Markov; Sustainable Development

Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model.

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.

Impact of an improved random forest-based financial management model on the effectiveness of corporate sustainability decisions

With the development of the economy, more and more electronic manufacturing enterprises are emerging like mushrooms after rain. These enterprises, while developing, also face financial risks caused by various reasons. In order to provide early warning for financial risks of enterprises, improve the accuracy of identifying financial risks, avoid financial crises, and provide assistance for sustainable development decisions, this paper proposes a financial management model based on modified random forest. In order to improve the generalization ability of financial management models, pruning methods were adopted in the study to avoid overfitting. Synthetic minority oversampling technique is used to optimize the financial management model and reduce the calculation deviation of the model through its sampling ability. At the same time, the prediction index system is proposed to improve the analysis ability of the financial management model. The results show that the accuracy and recall rate of the improved algorithm based on random forest proposed in this study in identifying corporate financial distress are 98.03 % and 100 % respectively. The importance value of operating income and cash flow in enterprise indicators is 0.391, which is the most relevant indicator for enterprise financial forecasting. The results show that after the improvement of synthetic minority oversampling technique, the stochastic forest model can effectively improve the recognition and early warning ability of enterprises’ financial distress, and is conducive to maintaining good operating efficiency and sustainable operation of enterprises. Electronic manufacturing enterprises need to strengthen their attention to cash flow, improve their cash flow, and enhance their profitability. The financial management model designed by the research institute can provide technical and information support for financial early warning and sustainable development of electronic manufacturing enterprises.

The Water Resources Rebound Effect Threatening the Achievement of Sustainable Development Goal 6 (SDG 6)

Coaction of climate change and human activities exerts a significant impact on the fate of water resources, provoking the rebound effect in water resources and threatening the achievement of SDG (sustainable development goal) 6. However, the mechanisms of interactions between this effect and SDG 6 and how to decrease this effect towards the achievement of SDG 6 are less known. In this paper, a water resources rebound effect (WRRE) model is integrated into a river basin sustainable development decision support system to simulate and project the interactions between the WRRE and SDG 6 under multiple combination scenarios. The results show that multiple drivers, including precipitation, technological advances, and ecological water flow, coaffect the WRRE, not the silo technology factor. The transfer fate of the saved water is a major root cause leading to the WRRE and harming the achievement of SDG 6, and the WRRE is positively correlated to SDG 6 overall, which, nevertheless, can sometimes be reversed by technological advances. Our outcomes indicate that decoupling SDG 6 from the WRRE can promote the achievement of SDG 6 and decrease the rebound effect, relying on holistic integration that couples climatic, socioeconomic, and behavioral interaction between SDG 6 and the WRRE and translation of scientific research into available decision-making information for strict scientific-research-based policy actions.

Some modeling challenges in dynamic life cycle assessment

Life cycle assessment (LCA) has been a mainstream tool to evaluate the environmental impacts of products, services, and systems. Current LCAs inherently rely on the static basis and commonly fail to include temporal considerations. To better assist in the decision-making for sustainable development, dynamic LCA has been initiated to answer more complex and interdisciplinary questions. As in its initial phase, dynamic LCA faces many modeling challenges that at the same time are meaningful research opportunities. In modeling dynamic LCA, there are several key aspects that need more attention for contribution and close collaboration across the first three phases of the LCA framework.

The influence of gender specific decisions on household technology choice within the farming households in central Uganda

How households make decisions, who influences decision making and how members bargain over resources and opportunities greatly affects the livelihood and wellbeing of members within a given household. This study sought to understand gender specific decisions and their influence on household technology choice, packaging and adoption of agricultural technologies. Growing Bananas with Trees and Livestock (GBTL) technology system was implemented by National Agricultural Research Organisation and Bioversity International in three districts of Central Uganda: Kiboga, Nakaseke and Ssembabule. Using Principal Component Analysis (PCA) and Cluster Analysis (CA), typologies were created in which two distinct clusters of farming households were revealed. Using a bargaining model of technology adoption, three types of households were identified; male and female only, female only and male only headed households. For technology uptake, either a single component, two components or all components of a technology was adopted. Results indicated that Household size, age, land owned, labour and gender composition of the household positively influenced the adoption of the technology. Farm households were able to take up and adopt components of the technology that were equivalent to the available household resources. Technology choice and adoption was influenced by available land, labor and household objectives (food, income and nutrition security). Households' ability to avert the loss of production and/or assets was very varied and depended on household size. For non-business home expenses, decisions followed a gendered perspective where households with both male and female, had more participants involved in household decision making, households make decision jointly when it comes to purchases and sales. Banana plantations establishment and management was for the spouse. Livestock production was initiated and managed by the household heads. Individual decisions were influenced by age of the household head, livestock owner. Joint household’s decision making was significant on purchases, sales of inputs and outputs, land ownership, and were influenced by household size and farm size. This study brings out important policy implications that in order to ensure widespread adoption of improved technologies, there should be equitable access to complementary inputs, especially land and labour for females. And technology packaging should take into consideration the gender specific decisions for sustainable agricultural development. Key words: Decision making, Technology choice, adoption, Coffee banana farming system, Gender

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis.

Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.

Contributions of the IUCN Red List of Ecosystems to risk-based design and management of protected and conserved areas in Africa.

Protected and conserved areas (PCAs) are key ecosystem management tools for conserving biodiversity and sustaining ecosystem services and social cobenefits. As countries adopt a 30% target for protection of land and sea under the Global Biodiversity Framework of the United Nations Convention on Biological Diversity, a critical question emerging is, which 30%? A risk-based answer to this question is that the 30% that returns the greatest reductions in risks of species extinction and ecosystem collapse should be protected. The International Union for Conservation of Nature (IUCN) Red List protocols provide practical methods for assessing these risks. All species, including humans, depend on the integrity of ecosystems for their well-being and survival. Africa is strategically important for ecosystem management due to convergence of high ecosystem diversity, intense pressures, and high levels of human dependency on nature. We reviewed the outcomes (e.g., applications of ecosystem red-list assessments to protected-area design, conservation planning, and management) of a symposium at the inaugural African Protected Areas Congress convened to discuss roles of the IUCN Red List of Ecosystems in the design and management of PCAs. Recent progress was made in ecosystem assessment, with 920 ecosystem types assessed against the IUCN Red List criteria across 21 countries. Although these ecosystems spanned a diversity of environments across the continent, the greatest thematic gaps were for freshwater, marine, and subterranean realms, and large geographic gaps existed in North Africa and parts of West and East Africa. Assessment projects were implemented by a diverse community of government agencies, nongovernmental organizations, and researchers. The assessments have influenced policy and management by informing extensions to and management of formal protected area networks supporting decision-making for sustainable development, and informing ecosystem conservation and threat abatement within boundaries of PCAs and in surrounding landscapes and seascapes. We recommend further integration of risk assessments in environmental policy and enhanced investment in ecosystem red-list assessment to fill current gaps.

Potential thermal constraints for sustainable industrial city: refined simulation and prediction of extreme urban heat island effect

Extreme urban heat islands (E-UHIs) have caused radical changes in urban climates. The lack of refined extreme thermal environment simulation models (ETESM) hinders research on the spatial evolution of E-UHI, which increases the uncertainty for managers to make sustainable decisions. Here, considering both the heating of industrial land and the heat transfer of surrounding facilities, a refined ETESM is constructed. Assuming four scenarios: maintain current trend (MtS), economically oriented scenario (EcS), environmentally oriented scenario (EnS), green transformation scenario (GtS), the future potential constraints of the E-UHI are simulated and predicted, thus determining the sustainable development decision in industrial city. The results indicate that there is significant spatial consistency between simulated temperature and actual temperature at both city scale (Pearson'r = 0.924, P<0.001) and industrial land scale (Pearson'r = 0.948, P<0.001). The average temperature under four scenarios shows: EcS>MtS>GtS>EnS. The average SST of EnS and GtS was 1.36℃ and 1.06℃ lower than that of MtS, respectively. Considering the dual constraints of environmental livability and economic sustainability, GtS is the best decision to achieve the sustainable development in industrial city. This simulation-driven study provides support for E-UHI mitigation and sustainable development of industrial city.

Integrated Reporting and Group Decision-Making for Sustainable Development: A Bibliometric Review

The demands of society are constantly evolving, imposing significant pressures on business organizations within the context of sustainable development policy. As a result, organizations have begun to move towards promoting information disclosure through integrated reporting, transitioning from sustainability reports to financial reports. Simultaneously, decisions made to address specific problems are no longer the sole responsibility of a single individual. Challenges now revolve around group decision-making with the aim of achieving a consensus solution. To comprehend the landscape of integrated reporting and group decision-making in the scientific literature concerning sustainable development, a bibliometric review was conducted across major scientific databases from 2008 to 2023. The analysis utilized Bibliometrix, based on the R language, and Biblioshiny for graphic generation. The results indicate a quantitative increase in publications addressing integrated reporting and group decision-making in the context of sustainable development. However, a thematic isolation among existing studies was evident, pointing to an opportunity for future research to bridge the gap and establish more robust connections between the subjects. &#x0D; Keywords: integrated reporting, group decision-making, sustainable development, bibliometric review&#x0D;

Interplay of network information dissemination in the era of big data on environmental sustainable development and agricultural consumers' purchase decisions

The rapid progress of technology has led to a proliferation of information sources and distribution channels, facilitating the dissemination of knowledge on environmental sustainability and agricultural practices to consumers. However, the intricate impact of Network Information Dissemination (NID) on environmental sustainability and consumer purchasing decisions in agriculture remains a complex area of study. This research aims to explore the interconnections between network information dissemination, environmental sustainability, agricultural consumer purchase decisions, and the mediating role of information overload and trust in information sources. Additionally, it investigates the moderating impact of information accessibility. Employing a quantitative research design, data is collected through an online survey targeted at Chinese consumers, with 400 responses being evaluated using SmartPLS. The findings reveal that network information distribution significantly and positively influences both environmental sustainability and agricultural consumer purchasing decisions. Moreover, the study identifies that information overload and trust in information sources act as moderators in the relationship between network information diffusion and agricultural consumer purchasing decisions. Additionally, the research indicates that information accessibility moderates the relationship between network information distribution and environmental sustainability. Specifically, when information accessibility is high, the relationship is more pronounced compared to situations where accessibility is low. This underscores the importance of information accessibility in achieving environmental sustainability through network information transmission.