Climate change is affecting ecosystems, communities, and human health worldwide. These changes pose risks to global energy systems so there is a dire need to combat climate change and limit global warming to 1.5°C. This study undertake global energy systems and forecasted total energy consumption, production and greenhouse gas (GHG) emissions worldwide for the study period 2021–2050 by taking the input data from 1970 to 2020 using the four algorithm’s namely, Holt Winter (HW), Exponential Smoothing (ES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) implemented in Python. It is found that HW and ES have same forecast results globally with energy consumption of 236,285 TWh which can easily meet by 475,980 TWh generation until 2050. Renewables and fossil fuels contributed to 250,106 TWh units and 225,874 TWh units with 48 billion metric tons of GHG emissions until 2050. The global forecast of ARIMA model suggested that 232,878 TWh energy consumption is noticed which can easily meet by 446,126 TWh generation with 213,052 TWh share of renewables and 233,074 TWh of fossil fuels with 49 billion metric tons of GHG emissions produced until 2050. SARIMA model forecast is very much valuable for limiting global mean temperature to 1.5 °C. The global energy consumption is forecasted to be 231,022 TWh which easily meet by 350,054 TWh green energy generation potential with almost zero emissions until 2050 and it is found that SARIMA model has 98% of accuracy. • Python based algorithms are used to achieve clean energy targets globally (1970–2050). • Forecasted global energy consumption, production, and GHG emissions using holt winter, exponential smoothing, ARIMA and SARIMA. • By 2040, renewable energy is projected to 100% in global power mix under SARIMA model. • Global CO 2 emissions must decrease by 60% in 2035 and 100% in 2040 under SARIMA model. • The transition to sustainable energy system is expected to boost global GDP by 15% until 2040.

Reducing carbon dioxide emissions (CO2e) is essential to achieving sustainable development objectives, safeguarding the environment, reducing the effects of climate change, and maintaining biodiversity for a future that is cleaner and more resilient. Nowadays, environmentalists also focus on how the environment reacts to society's increasing level of education. Increasing public awareness of environmental deterioration through environmental education, moral sermons, and higher tertiary enrollment can be a crucial policy in the fight against global warming, along with other measures to reduce CO2e. The effort to combat climate change necessitates improving energy efficiency (EE) and information and communication technology (ICT). Therefore, this study examines the impact of higher education (HED), EE and ICT on CO2e under the N-shaped EKC hypothesis. Using the panel data for five BRICS nations between 1991 and 2023, an empirical analysis is carried out, and the coefficients of the variables are estimated using the Second generation techniques (cross-sectional augmented distributed lag (CS-ARDL), Common Correlated Effects Mean Group (CCEMG) and Augmented mean group (AMG) approach. The estimates confirm the Inverted N-shaped EKC hypothesis between the GDP and CO2e. Moreover, the long-run estimates reveal that higher education, energy efficiency and ICT have negative effects on CO2e. BRICS countries should promote environmental education across all tiers, with an emphasis on conservation, climate change mitigation, and sustainable development, to help improve environmental awareness and literacy. Moreover, they should decouple energy use from economic growth to simultaneously achieve both economic and environmental goals, which can be facilitated by increasing ICT utilization, promoting higher tertiary enrollment, and improving energy efficiency. • This study examines the impact of Digitalization, Higher Education, and Energy Efficiency on environmental sustainability. • This study investigates the the N-Shaped Environmental Kuznets Curve in the BRICS economies. • This study utilizes the CS-ARDL, CCEMG and AMG approaches. • The finding shows there exist an Inverted N-shaped EKC hypothesis between the GDP and CO2 emissions. • The digitalization, energy efficiency, and higher education have negative effect on CO2 emissions.

Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings –that are major contributors to energy use– is therefore a key priority, particularly in regions with outdated building stock. Artificial Intelligence (AI) and Machine Learning (ML) can automate retrofit decision-making and find retrofit strategies. However, their implementation faces challenges of data availability, trust and alignment with trustworthiness guidelines, as well as compliance to AI regulations. This paper presents a trustworthy-by-design ML-based decision support framework that recommends energy efficiency strategies for residential buildings using minimal user-accessible inputs. The framework employs Conditional Tabular Generative Adversarial Networks (CTGAN) to augment limited and imbalanced data, while neural network-based multi-label classifier identifies potential combinations of retrofit measures. An Explainable AI (XAI) layer using SHAP is also incorporated to clarify the rationale behind recommendations, validate the model, and guide feature engineering. Two case studies on distinct datasets validate performance and replicability: i) a well-established, large Energy Performance Certificate (EPC) dataset for England and Wales; ii) an imbalanced post-retrofit dataset from Latvia (RETROFIT-LAT). Results demonstrate that the framework can handle diverse data conditions and improve performance up to 53% compared to the baseline model without XAI and synthetic data generation. Overall, the proposed framework provides a novel, user-friendly classification-based solution for building retrofit decision support that incorporates the trustworthiness aspects of transparency, human oversight, data governance, and fairness and aids stakeholders in achieving effective energy efficiency investments while aligning with AI regulation and ethical standards.

The urgent need to combat climate change, reduce greenhouse gas emissions, and transition to renewable energy sources motivates government entities to implement regulations for power stakeholders, ensuring a greener and more affordable energy market in supply chains. Motivated by the demand to align energy supply systems with strategic government interventions, this study hypothesizes that targeted regulatory tools can effectively coordinate stakeholder behavior to accelerate green energy integration. In this regard, we examine the game-theoretic factors shaping the decisions of the government, suppliers, and retailers towards power grid implementation, emphasizing the government’s regulatory influence through four key policies: tax, subsidy, green, and research and development. Methodologically, three novel game models are introduced: a Nash game that encourages overall cooperation, the first non-cooperative game that supports a coalition between the government and suppliers against retailers, and the second non-cooperative game that promotes a coalition between the government and retailers against suppliers. Using a Canadian case study and sample data, we apply grey wolf optimization, artificial bee colony, and particle swarm optimization to estimate stakeholder equilibrium strategies towards power grid implementation. Results indicate that (1) first-game coalition; (2) minimum energy price thresholds; (3) integrated green energy planning; and (4) the stable tax policy contribute positively to the construction of a green and sustainable power grid in the region. The findings provide practical policy insights, guiding governments in the development of targeted fiscal instruments, promoting stakeholder collaboration, and ensuring regulatory frameworks are consistent with long-term energy transition objectives. • A green and sustainable power grid application is promoted under government policies. • Government considers four policies: tax, subsidy, green and R&D in the application. • Government adds green and social welfare contributions to the power grid application. • Three novel game models are proposed for stakeholder cooperation and coalition. • The application is implemented in a case study of a power supply chain in Canada.

This paper addresses the critical issue of decarbonising healthcare systems to help combat climate change. I focus on identifying the 'agents of justice' responsible for this transformation. Beginning with the claim that healthcare's greenhouse gas emissions cause injustice, the paper assumes that achieving a net zero healthcare system is essential for climate justice. The discussion centres on two prevailing perspectives: one that primarily assigns responsibility to healthcare organisations and another that holds individual healthcare professionals accountable. The paper advocates for a pluralistic approach to responsibility, contending that the complexity and scale of reducing healthcare emissions necessitate allocating responsibilities based on effectiveness. This leads to the identification of two types of responsibility: first-order responsibilities, which involve direct actions to reduce emissions, and second-order responsibilities, which involve supporting and ensuring the fulfilment of first-order duties. The paper clarifies how mitigation responsibilities should be allocated across organisations and individuals by expanding the scope of responsibility to include a broader range of agents, both within and beyond the healthcare sector. By distinguishing between first-order and second-order responsibilities, the paper offers a clearer framework for understanding the distribution of obligations in achieving climate justice in healthcare. Ultimately, it underscores that focusing solely on direct mitigation efforts by organisations or clinicians is inadequate, and a more comprehensive, multi-agent approach is required to effectively decarbonise healthcare systems.

Purpose of research. The Russian Federation and the Republic of Kazakhstan are member countries of the Kyoto Protocol, an international agreement aimed at reducing greenhouse gas emissions in order to combat climate change. This requires the formation of a model bank for automated management of the allocation of quotas for greenhouse gas emissions into the atmosphere. Methods. Based on the analysis of carbon accounting features, a new trading methodology based on Blockchain technology and the VCG algorithm is proposed, aimed at increasing transparency, security and automation of transactions between market participants. A method and algorithm for implementing carbon emission quotas through the auction system has been developed, which ensures the openness and transparency of the distribution process. Results. In this paper, we have modeled a digital platform for automated carbon emissions trading using Blockchain technology. A study of the existing tools and mechanisms of the carbon emissions quota market has been carried out, its main problems have been identified, such as insufficient transparency of transactions, the risk of fraud and difficulties in monitoring compliance with regulatory requirements. As part of the simulation, smart contracts were developed and tested, which automate the fulfillment of transaction conditions and transaction accounting. The use of Blockchain technology makes it possible to ensure high information security, control the course of trading and prevent unauthorized data changes. An automated system for accounting and conducting transactions increases the efficiency of quota management and reduces transaction costs. Conclusion. The developed prototype of the digital platform promotes the development of an environmentally responsible economy, encourages enterprises to reduce their carbon footprint, and ensures transparency and security in trading environmental quotas. As a result, the proposed model increases the confidence of market participants and contributes to the formation of a sustainable system for regulating greenhouse gas emissions.

This project presents an Advance Movable Electric Vehicle (EV) Charging Station powered by a solar photovoltaic (PV) system to reduce grid dependency and promote renewable energy use. The system uses an ARDUINO-based controller integrated with RFID, GSM, and GPS modules for smart monitoring and user authentication. A SEPIC converter is employed to maintain a stable DC output across varying solar inputs, ensuring efficient battery charging. The design is simulated in Proteus software and a 5W hardware prototype is developed and tested, demonstrating reliable and portable EV charging performance..The rise of electric vehicles (EVs) signals a promising shift towards sustainable transportation,offering a solution to combat climate change and reduce reliance on fossil fuels. However, challenges persist in the charging infrastructure, with conventional stationary charging stations causing significant waiting times for vehicle owners. This hinders the seamless integration of EVs into mainstream transportation systems. To address this issue, our project aims to develop an innovative solution: the Solar Wireless Charging System in Electric Vehicles. By harnessing Keywords Portable EV Charger Mobile Charging Station DC Fast Charging (DCFC) On-the-go Charging Off-grid Charging Solution Emergency EV Charging High-power Mobile Charging Advanced and Technological Keywords

ABSTRACTAgricultural soils are the largest human‐induced source of nitrous oxide (N2O) due to the extensive fertilizer use in crop production. Despite progress made in global N2O budget accounting, using the IPCC‐derived static emission factors (EF) limits our capability to project future changes in agricultural N2O emissions and identify cost‐effective mitigation strategies. Here, we use a physics‐informed AI‐driven dynamic EF modeling framework to project direct agricultural soil N2O emissions induced by mineral fertilizer additions under various climate and nitrogen (N) regulation policy scenarios. Compared to projections based on static EF, our study yields higher future N2O emissions by 0.3–1.1 Tg N year−1 across scenarios by 2050, implying greater abatement needs to combat climate change. The gap between static and dynamic EF approaches is projected to widen to 29%–34% by mid‐century. Under moderate‐ to high‐ambition N regulation policies, we project that a 25% emission reduction can be reached before 2050, while a 45% reduction is only attainable under high‐ambition N policies and strong climate action. The potential for N2O mitigation due to a policy shift varies substantially among regions, with seven top source regions contributing 76%–87% of global N2O reduction. Adopting N2O reduction technologies in hotspot areas would significantly accelerate the timeline for achieving the 25% reduction goal. Improving fertilizer management on croplands can provide climate benefits comparable to, or even exceeding, those of enhancing soil carbon sequestration, particularly in regions with low nitrogen use efficiency. Our findings highlight the higher mitigation potential of targeting N2O emission hotpots and the urgency of implementing policy shifts.

Water-saving devices (WSDs) are well-known examples of Environmentally Sound Technologies (ESTs) used for water conservation and waste reduction. This work presents the development of an Internet of Things (IoT) based EST prototype of a WSD system for mitigationdriven shower water management in Niamey hotels. The developed Smart Water Use and Control Meter (SWUCOM) device was tested for three (3) days. The results show that the average daily shower water per guest in Niamey hotels is estimated at 92.07L. Furthermore, when making assumptions in some scenarios, it is demonstrated that SWUCOM can reduce the amount of shower water consumed by up to 45.70%. With its various features, the use of SWUCOM goes beyond shower water management. The paper therefore also highlights SWUCOM potential use as a smart meter in water distribution and consumption in cities and in irrigated agriculture, and its envisaged improvement to target smart cities and smart agriculture development in West Africa.

Climate change is a global issue that poses potential risks to breastfeeding practices and outcomes. Therefore, it is important for breastfeeding mothers to understand the changes caused by climate change and what they mean. This study aimed to examine in depth the effects of climate change on breastfeeding mothers' experiences and to explore their perspectives on how environmental changes are reflected in their breastfeeding processes. This study used a qualitative descriptive design and an inductive approach. The interviews continued until data saturation was reached, and 19 breastfeeding mothers living in Türkiye completed the study. The interviews were conducted via telephone. Thematic analysis was used to analyse the data. Through the thematic analysis of the interviews, four main themes and 14 subthemes were identified. The themes were: 1) impacts of climate crisis, 2) breastfeeding negatively affected by climate crisis, 3) increasing breastfeeding to mitigate the effects of climate crisis, and 4) coping with climate crisis. Our study found that climate change and related events affect mothers' breastfeeding experiences and cause them to worry about long-term negative health outcomes such as food insecurity and economic and psychological problems. In line with the goals of combating climate change and reducing resource consumption, breastfeeding promotion should be strengthened through policy development and awareness-raising strategies.

Introduction Solar energy plays a critical role in meeting global energy demands and combating climate change. While meteorological factors are widely used in solar radiation forecasting models, the impact of air pollution parameters remains underexplored, particularly in the Western Mediterranean Region of Türkiye. Methods Regression models were developed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Extra Trees (ET), and LightGBM; under two scenarios: M1 (meteorological data only) and M2 (meteorological data combined with air pollution variables: PM10, PM2.5, SO 2 , CO, NO 2 , and NO X ). Data from three provinces (Antalya, Burdur, and Isparta) were analyzed. Model performance was evaluated using R 2 , RMSE, and MAPE metrics, with statistical significance assessed via Wilcoxon signed-rank tests on 10-fold cross-validation scores. Results Incorporating air pollution variables (M2 scenario) substantially enhanced forecasting accuracy across all provinces and algorithms (p = 0.001). The coefficient of determination reached R 2 = 0.86 in the best-performing models, with relative improvements exceeding 14% over the M1 scenario in Antalya. Ensemble methods (ET and LightGBM) consistently outperformed other algorithms. Feature importance analysis identified temperature and relative humidity as dominant predictors, while CO showed the highest importance among air pollution variables. Discussion These findings confirm that air pollution data constitutes an indispensable component for high-accuracy solar radiation forecasting in the Western Mediterranean Region. The results directly inform regional solar energy planning and power generation strategies, and highlight the need to incorporate air quality data into future forecasting frameworks.

In South Asia, many countries, including Pakistan are undergoing abrupt and unexpected changes in climate resulting in floods, erratic weather, less food production, and migration. The region has become one of the most adversely impacted across the globe, despite minimal contributions to global emissions. Many experts have warned that if the current pace of change in climate continues, the region might become unlivable in subsequent years. In order to tackle this growing threat of climate change, policymakers in Pakistan have begun taking several initiatives, including the development of the National Adaptation Plan in 2023 and establishment of an exclusive ministry for the climate crisis. Even though the government has introduced various plans, policies and programs, but the lack of adequate financial resources, interdepartmental coordination and political will are viewed as major challenges that can impede these efforts to combat climate variability. This study will be an attempt to evaluate the effects and consequences of climatic shifts in Pakistan and highlight the steps taken to curtail it. At the end it shall provide various suggestions and recommendations to combat climate change in Pakistan, through a qualitative methodology based on secondary data sources, and the strategies needed to make the country more climate resilient in the years to come.

To combat climate change, farmers want to develop sustainable agriculture that enhances food production while strengthening their capacity to cope with extreme weather events and pest and disease pressures. Promoting agroecological farming practices is a promising approach in enhancing sustainability and strengthening the climate-resilient farming systems. Recent research often overlooks to what extent the agroecological farming practices (AFP) provide a measurable advantage over non-AFP methods under increasing environmental challenges. In this regard, this study compares the extent of climate resilience between AFP mango-based farming systems and non-AFP mango-based farming systems in southern Ethiopia. AFP adopters applied ecological principles like intercropping, integrated pest management, agroforestry, canopy management, varietal diversity, and water and soil preservation to enhance biodiversity and soil health, and boost productivity and ecosystem services. The study employed a mixed-method design, drawing on the data from 395 selected households. The resilience of AFP and non-AFP farming systems was assessed by computing the 13 agroecosystem indicators of climate resilience using the Self-evaluation and Holistic Assessment of Climate Resilience of Farmers and Pastoralists (SHARP+) tool. Households in AFP mango-based farming system demonstrated greater diversification in agricultural production system compared to those in non-AFP mango farming system. The analysis of climate resilience indicators showed that the mango production systems under the AFP were more climate-robust than their conventional systems. Both the compound resilience score and the household resilience index showed that the mango farming systems under AFP substantially enhanced climate resilience. Hence, coordinated supports from the extension services, NGOs, and researchers are needed to scale up these benefits of AFP. Strengthening the AFP mango farming requires addressing the key barriers such as market access, input availability, and crop diversification strategies. This paper identifies important avenues for further AFP research in Sub-Saharan African countries.

The growing urgency to mitigate climate change and reduce energy demand has led to the emergence of zero-energy buildings (ZEBs) as a sustainable solution in the construction industry. This study focuses on modeling and simulating energy consumption in ZEBs to evaluate their performance and optimize their design. A zeroenergy building is defined as a structure that balances its energy consumption with onsite renewable energy generation over a defined period, achieving net-zero energy usage.The increasing global demand for energy-efficient and sustainable construction practices has positioned Zero-Energy Buildings (ZEBs) as a vital component of the effort to combat climate change and reduce energy consumption. A Zero-Energy Building is designed to achieve a balance between its annual energy consumption and on-site renewable energy generation, resulting in net-zero energy usage. This study delves into the modeling and simulation of energy consumption in ZEBs, aiming to analyze their performance, optimize energy efficiency, and provide insights for practical implementation. The study emphasizes the importance of passive design strategies, such as optimal site orientation, natural ventilation, day lighting, and the use of thermal mass, in significantly reducing baseline energy demand. Active measures, including highefficiency HVAC systems, smart energy management systems, and the adoption of intelligent building automation technologies, are evaluated for their role in bridging the energy consumption gap. Furthermore, the research addresses challenges related to intermittency in renewable energy generation, peak load demands, and occupant comfort, proposing innovative solutions such as hybrid energy systems and advanced control algorithms. The simulation incorporates varying climatic conditions, occupant behavior, and energy use profiles to ensure comprehensive analysis and practical applicability. The insights gained can assist architects, engineers, and policymakers in making informed decisions to enhance energy efficiency, reduce greenhouse gas emissions, and support the transition to sustainable built environments. By addressing technical, environmental, and economic aspects, this work underscores the feasibility and necessity of Zero- Energy Buildings in shaping a resilient and energy-conscious future.

How will academic meetings look in a future where we combat climate change?

Abstract Legume-Rhizobia symbiosis plays a crucial role in both agricultural and ecological contexts, providing a sustainable solution for improving soil fertility and mitigating climate change through carbon storage. This thorough examination delves into the complex mechanisms of this mutually beneficial relationship, investigating the molecular, physiological, and ecological aspects that govern the interaction between leguminous plants and nitrogen-fixing rhizobia bacteria. In this review, we analysed the key elements of this symbiosis, including the processes of nodulation, the pathways of communication, and the genetic factors that enable nitrogen fixation. Furthermore, we scrutinize the factors that influence the success of this partnership, which range from soil conditions and the diversity of host plants to the effectiveness of specific strains of rhizobia. Additionally, the broader implications of legume-rhizobia symbiosis on carbon storage were also reviewed. This mutualistic relationship not only enhances plant growth and productivity but also stimulates the release of substances from the roots that enrich the organic matter in the soil and contribute to long-term carbon retention. We assess the potential of this process to reduce greenhouse gas emissions and combat climate change, emphasizing its significance in sustainable agriculture and ecosystem management. Moreover, we also explore recent advancements in biotechnology and microbial engineering, which offer promising opportunities to optimize legume-rhizobia interactions for increased agricultural productivity and enhanced carbon storage. This comprehensive examination synthesizes the current state of knowledge on legume-rhizobia symbiosis and its role in carbon storage, shedding light on the multifaceted advantages of this ecological partnership for a sustainable and resilient future.

In the digital era characterized by rapid technological advancements, artificial intelligence (AI) has emerged as a tool with the potential to transform the sustainability management paradigm. This research aims to explore how the application of AI, specifically generative artificial intelligence (GenAI), can influence sustainability management strategies across various industrial sectors. While AI offers opportunities to increase efficiency and productivity, challenges related to environmental impacts, particularly in the context of increasing energy consumption, remain a major concern. Data from the International Energy Agency (IEA) indicates that the information and communications technology (ICT) sector accounts for nearly 4% of total global energy consumption, and with the increasing use of GenAI, this figure is expected to continue to rise. This research also highlights the relationship between the use of GenAI tools and users' environmental perceptions and behaviors, indicating that the more frequently individuals use GenAI tools, the more they perceive the impacts of climate change. Therefore, it is crucial to raise awareness about the environmental impacts of everyday technologies. This research also discusses the importance of collaboration between the public and private sectors in creating a framework that supports the sustainable use of AI, as well as the need to support small and medium-sized enterprises (SMEs) in adopting this technology. By integrating sustainability considerations into the development and implementation of AI technology, we can contribute to global efforts to combat climate change and ensure that technological advancements do not come at the expense of our planet.

Abstract Rotating Packed Beds (RPBs) represent a groundbreaking advancement in sustainable technology, playing a pivotal role in combating climate change and contributing indirectly to Society 5.0. Through innovative applications in carbon capture, resource efficiency, and waste management, RPBs demonstrate their potential to address critical environmental challenges, as well as promote responsible innovation in line with the United Nations Sustainable Development Goals (SDGs). Their compact design and high efficiency enable them to not only reduce emissions but also promote economic growth and enhance public health. This study explores the multifaceted roles of RPBs in achieving sustainable urban development, with a focus on their contributions to carbon dioxide capture, wastewater treatment, the circular economy, and scalability. By seamlessly integrating advanced engineering solutions with societal needs, RPBs exemplify how technological innovation can align with the transformative vision of Society 5.0, paving the way for a sustainable and resilient future.

Hybrid renewable energy systems (HRES) offer a sustainable and resilient solution for meeting domestic energy demands in remote regions and combating climate change. This study assessed the technical and economic feasibility of establishing a self-sufficient HRES for the remote tribal villages of Koopgarh, Madhya Pradesh, and Kurkheta, Jharkhand, India, as a case study. These regions, owing to their dense forest cover, are not connected to the national grid but offer significant potential for tapping into solar, wind, and micro hydropower. Hence, site-specific HRES configurations were proposed in this study, considering solar PV, Wind, Battery energy storage, and Diesel Generators, along with current grid connectivity. In addition to traditional metrics such as net present cost (NPC), levelized cost of energy (LCOE), and environmental emissions for HRES configurations, a sensitivity analysis considering future variations in resource availability and energy demands was presented to understand the influence of capital cost shortages on LCOE. The proposed hybrid photovoltaic-wind battery (PV-WT-B) system, supplemented with a battery energy storage system for improved efficiency and safety, is the most viable option. Simulation results show that the proposed HRES can achieve an LCOE of USD 0.193 per kilowatt-hour for Koopgarh and USD 0.033 per kilowatt-hour for Kurkheta, while significantly reducing the carbon footprint and promoting energy equity in rural areas. This approach goes beyond mere energy generation and creates a sustainable, self-reliant community that satisfies the current and future energy demands of the region.

Combatting climate change and promoting sustainable urban communities are urgent global goals aligned with the United Nations Sustainable Development Goals 11 and 13. Nature-based solutions (NbS) offer a promising opportunity to address climate challenges by integrating natural ecosystem services within urban development. Although research on NbS is growing, there is limited research on socio-economic factors, particularly public awareness, attitudes, and scepticism and specifically the role that social media influencers might play in promoting NbS initiatives. To address this gap, this study is among the first to integrate social media influencers and attachment theory into a Stimulus-Organism-Response framework to investigate how attachment to green influencers affects awareness, scepticism and attitudes towards NbS. Furthermore, the study provides a novel cross-country comparison of two distinct socio-ecological contexts: South Africa and Norway. Using multigroup structural equation modelling, data from 704 respondents who engage with green influencers were analysed. Results indicate that attachment to influencers is positively associated with awareness of NbS in both countries, but significantly negatively associated with scepticism in South Africa, while Norway displays a more reflective scepticism. These cross-country differences extend influencer marketing theory by demonstrating that influencer attachment does not uniformly reduce scepticism but interacts with socio-ecological context. By positioning green influencers as change agents for sustainability changes, this study contributes theoretically to environmental management and suggests that tailored influencer marketing strategies that foster awareness and critical engagement can effectively promote NbS adoption, supporting climate action and sustainable urban development aligned with SDG 11 and SDG 13.