The 2030 Agenda for Sustainable Development faces considerable implementation challenges in border regions, where natural and socio‑economic processes transcend administrative boundaries. This study assesses progress towards selected Sustainable Development Goals (SDGs) in two Russian border regions: the Republic of Buryatia and Zabaikalsky Krai, which share borders with China and Mongolia, and identifies directions for bilateral cooperation based on the transboundary challenges. Based on official data from the Federal State Statistics Service of Russia for 2010–2024, the authors analysed 39 indicators covering SDGs 6, 8, 11, 13, 15 and 17. Growth rates were calculated and compared with national averages. The results reveal contrasting development models: Zabaikalsky Krai shows strong economic growth (gross regional product per capita index 114.3% in 2023) but critical deficits in access to quality drinking water (58.1% vs. national 89.2%), safe sanitation (49.2%), buses for persons with reduced mobility (3.2%), and protected forest areas (0.94%). The Republic of Buryatia performs better in social and environmental spheres (urban water access 86.0%, sanitation 80.5%, equipped buses 61.9%, reforestation ratio 247%) but lags economically—gross regional product (GRP) per capita index at 101.1%—and faces unique constraints from strict Baikal environmental norms (only 2.5% of wastewater meets those standards). Both regions suffer extreme wildfires and dispose of almost all municipal solid waste in landfills without recycling. The conclusions indicate that joint bilateral actions should include harmonised environmental standards and monitoring methodologies, creation of cross‑border demonstration zones, and coordinated wildfire and water management. These findings can inform regional strategies, bilateral environmental projects, and statistical harmonisation within BRICS and the Shanghai Cooperation Organisation.

This study aims to inform evidence-based policy for climate change adaptation in the Philippines by estimating the climate sensitivity of household electricity demand among select cities in Metro Manila during the COVID-19 pandemic. Localized climate data from 2001 to 2021 were matched to households based on their city of residence to construct measures of climate variability. Heat Index (HI), which combines temperature and relative humidity, provides a measure of perceived thermal discomfort. Cooling Degree Days (CDD), defined as the number of days when daily HI exceeds a long-run threshold of 34.82 °C (the 75th percentile of historical HI), serve as another indicator of exposure to extreme heat conditions. Correlated random effects (Mundlak) regressions reveal strong climate sensitivity of electricity use. Household electricity consumption increases when high temperature coincides with high humidity. A 1 °C rise in HI leads to a 19 kWh increase in monthly electricity use, while an additional CDD raises monthly consumption by about 6 kWh. The estimated positive relationship between temperature, humidity, and electricity consumption is consistent with increased demand for cooling services during periods of higher thermal stress. Overall, the results suggest that households adjust their electricity consumption in response to perceived heat conditions through a range of cooling-related behaviors. However, electricity demand among lower-income households is significantly less responsive to climate fluctuations, which may indicate limited adaptive capacity amidst greater thermal discomfort. Conversely, higher-income households exhibit greater responsiveness, highlighting their potential for targeted adoption of energy-efficient appliances and rooftop solar systems. The results underscore the importance of climate-sensitive energy policies that simultaneously advance climate mitigation goals and address equity concerns.

University students, as a key youth consumer demographic, will play a vital role in shaping sustainable purchasing behavior in the future. This study aims to uncover the factors influencing students’ intention to minimize food waste at universities using the extended norm activation theory. An online survey of 664 students examined intentions to reduce food waste on campus. Of these, 245 students used online food delivery (OFD), while 419 engaged in in-canteen dining (IC). To evaluate the empirical data, this study utilized a partial least squares structural equation model and executed measurement invariance testing within the composite model. The empirical results demonstrate that the activation of personal norms is driven by awareness of consequence and the ascription of responsibility, which consequently has a direct impact on the intention to reduce food waste. Personal norms also indirectly influence the intention to minimize food waste. Students who purchased meals only reported weaker personal norms and lower intention to reduce food waste than those who ate in the canteen. However, the OFD group showed greater awareness of consequence, which supported their efforts to reduce food waste, compared with the IC group. Overall, this study provides further insight into the psychological mechanisms underlying sustainable food consumption among university students.

An Environmental, Social, and Governance (ESG) report is an essential information source for evaluating a company’s performance in sustainability practices. Organizations structure their environmental impacts, social responsibilities, and governance practices within a defined framework. This standardization is provided by the Global Reporting Initiative (GRI), which constitutes an internationally recognized guideline for sustainability reporting. Traditional reporting workflows are time-consuming for organizations and prone to data-entry errors, which limits the reliability of disclosed information. In this context, leveraging the capabilities of Large Language Models (LLMs) offers significant time and resource savings. This study uses the Llama-3.1-8B-Instruct model under two scenarios, Retrieval-Augmented Generation (RAG) and Low-Rank Adaptation (LoRA) fine-tuning, to analyze 30 food-sector ESG reports and produce ESG summaries, SWOT analyses, and GRI-aligned recommendations. The two approaches are evaluated on a stratified hold-out set of 6 unseen test reports (24 reports used for training) under a fair, matched-budget setup in which RAG retrieves the target report at inference. On four quality metrics, LoRA achieved higher mean scores than RAG; however, statistically significant differences were observed in only 4 of the 12 task–metric comparisons. Token usage was comparable, whereas RAG was substantially faster at inference. Rather than favoring one approach over the other, these findings reveal a trade-off between output quality and computational efficiency: LoRA yields quality gains on specific metrics, whereas RAG is substantially more efficient at inference. Given the limited size of the held-out test set, these results should be interpreted with caution.

Coastlines host dense human activity that concentrates combustion and elevates carbon monoxide (CO) and nitrogen dioxide (NO₂) burdens. Yet complex coastal meteorology often limits ground monitoring. This study addresses this gap with a multi-year, dual-pollutant, jurisdiction-scale analysis using a transparent Sentinel-5P column-burden workflow. This work employs the workflow on Canada’s Nova Scotia (NS), a cool and relatively stable North Atlantic coast, and the US state of Louisiana (LA), a warm-humid Gulf coast with one of the densest refining hubs, providing two contrasting coastal domains. The present analysis evaluates 2019–2024 tropospheric column CO and NO₂, applies uniform quality-assured screening, generates time series composites at native resolution, classifies spatial fields with Jenks Natural Breaks, and examines temporal trends. Columns are compared with inventories and ground networks as consistency checks. Six-year means highlight persistent contrasts: NS’s column CO is slightly higher than LA’s (0.0338 vs. 0.0321 mol m⁻²), and NS’s NO₂ is ≈ 2.5× LA’s (6.09×10⁻⁵ vs. 2.39×10⁻⁵ mol m⁻²). In NS, NO₂ peaks in summer, while CO reaches its highest seasonal mean in spring; in LA, NO₂ peaks in winter and CO peaks in spring. Recurring hotspots appear over Halifax-Dartmouth and North Sydney, and along the Baton Rouge-New Orleans corridor and northern parishes. These patterns may reflect a combined influence of coastal setting, seasonal atmospheric structure, and local activity, although direct meteorological attribution was not performed. By integrating satellite archives with ground networks, the study provides a reproducible, auditable approach that translates seasonal column dynamics into jurisdiction-ready evidence for evaluation calendars and corridor-focused siting, improving the timing and targeting of coastal air-quality management, and supporting United Nations Sustainable Development Goals (SDGs) 3 and 11.