Adoption Of Low-carbon Technologies Research Articles

Environmental Awareness and Social Sustainability: Insights from an Agent-Based Model with Social Learning and Individual Heterogeneity

Social sustainability requires both technological innovations and societal changes within energy systems, with decentralization playing a critical role. This shift emphasizes the increasing importance of individual user decision-making, posing significant management challenges. An individual’s environmental awareness has a key influence on their energy decisions. However, the relationship between individual environmental awareness and social sustainability, particularly from a systemic perspective, remains underexplored. Our study uses agent-based modeling to examine this relationship within Japan’s electricity market, focusing on social learning and consumer heterogeneity. We find that social learning leads to the formation of consumer clusters with specific electricity preferences, affecting environmental awareness differently across high- and low-carbon groups. This process reveals the nuanced role of social learning in promoting low-carbon technology adoption, which varies according to the market share of low-carbon energy. Additionally, our results suggest that initial heterogeneity in environmental awareness among consumers has a limited and varied effect on sustainable transition pathways. However, the diversity resulting from social learning significantly shapes these trajectories. These insights highlight the complex interplay between individual behaviors, societal dynamics, and technological advancements in steering the sustainable transition, providing valuable considerations for future energy system management.

Carbon footprint analysis of sugar production in Poland

Food production is a major contributor to greenhouse gas emissions and biodiversity loss, highlighting the need for a comprehensive approach to identify and reduce emissions. Efficient energy use is critical, alongside the adoption of low-carbon technologies that help agriculture and food processing adapt to climate change. Carbon footprint (CF) analysis is a key tool for assessing the environmental impact of food production and distribution, requiring a thorough evaluation of each product's life cycle from production to consumption. This study focused on the sugar production CF in three Polish plants, examining technological processes and creating unit process diagrams of the production cycle. This analysis led to the development of a database to calculate the CF based on production volume. The determined CF was 0.14–0.27 kg CO2eq/kg, and the average CFav: for plant 1–0.18; for plant 2–0.19; for plant 3–0.19 kg CO2eq/kg. Continuous monitoring is essential, allowing production practices to adapt to changing conditions and ensuring quick responses to sustainability needs. Reducing the sugar production CF involves several strategies, including adopting sustainable cultivation practices, optimizing production processes, using renewable energy sources, improving transportation efficiency, and minimizing waste. Together, these measures promote more environmentally responsible sugar production. By prioritizing sustainability and embracing innovative solutions, the food industry can significantly reduce its environmental impact, meeting the challenges of climate change and biodiversity loss.

Dual carbon goal and agriculture in China: Exploring key factors influencing farmers’ behavior in adopting low carbon technologies

Identifying the factors influencing farmers’ adoption of low-carbon technologies (FA) and understanding their impacts are essential for shaping effective agricultural policies amied at emission reduction and carbon sequestration in China. This study employs a meta-analysis of 122 empirical studies, delves into 23 driving factors affecting FA and addresses the inconsistencies present in the existing literature. We systematically examine the effect size, source of heterogeneity, and time-accumulation effect of the driving factors on FA. We find that significant heterogeneity in the factors influencing FA, except for farming experience, sources of heterogeneity from the survey zone, methodology model, technological attributes, report source, financial support, and the sampling year. Additionally, age, farming experience, and adoption cost negatively correlate with FA. In contrast, educational level, health status, technical training, economic and welfare cognition, land contract, soil quality, terrain, information accessibility, demonstration, government promotion, government regulation, government support, agricultural cooperatives member, peer effect, and agricultural income ratio demonstrate a positive correlation. Especially, demonstration and age show a particularly strong correlation. Finally, the effect of demonstration, age, economic and welfare cognition, farming experience, land contract, soil quality, information accessibility, government promotion, and support, as well as agricultural cooperative membership and peer effects on FA, are generally stable but exhibit varying degrees of attenuation over time. The effect of village cadre, family income, farm scale, gender, health status, technical training, and off-farm work on FA show notable temporal shifts and maintain a weak correlation with FA. This study contributes to shaping China's current low-carbon agriculture policies across various regions. It encourages policymakers to comprehensively consider the stability of key factors, other potential factors, technological attributes, rural socio-economic context, and their interrelations.

Optimization model of low-carbon technology adoption timing for coal power under decarbonization and flexibility demand: Empirical study in Beijing-Tianjin-Hebei region, China

Substituting renewable energy for coal power under the carbon neutrality goal is an inevitable path for power system transformation, while the high share of renewable energy leads to a rapid rise in system flexibility demand. Currently, coal-fired power is still one of the most economical and reliable large-scale flexible resources in China. Therefore, the issue of how to mitigate the conflict between cleanliness and flexibility for coal-fired power is urgent to be solved. Accordingly, this paper established an optimization framework for the retirement and technology adoption of coal-fired power units with high spatial-temporal resolution, and the regional carbon emission budget is estimated from a top-down perspective. Secondly, a dynamic carbon quota allocation method considering technological innovation and adoption is proposed. The carbon trading cost of the units is evaluated from a bottom-up perspective, to realize the interaction between the obligatory policy constraints of the top-level target and the flexible incentives of the market mechanism. Finally, the empirical analysis was conducted in the Beijing-Tianjin-Hebei region of China. The optimal solution achieved by proposed method results in a substantial 27% reduction in total costs while keeping the increase in carbon emissions below 3%. By analyzing the dynamic evolution path of coal and the scale of diffusion of multi-type technologies, the differences in the roles coal power assuming at various stages of decarbonization are discussed. The robustness of the model and the validity of the proposed method were verified by comparing the dynamic/static carbon quota allocation. Overall, this study provides an effective optimization model for investigating the low-carbon technology adoption timing for coal power in Beijing-Tianjin-Hebei region, and offers valuable insights for other coal-dominated emerging economies seeking for power system transformation roadmap in the forthcoming net-zero carbon age.

Green or greedy: the relationship between perceived benefits and homeowners’ intention to adopt residential low-carbon technologies

Transitioning to a net-zero economy requires a nuanced understanding of homeowners’ decision-making pathways when considering the adoption of Low Carbon Technologies (LCTs). These LCTs present both personal and collective benefits, with positive perceptions critically influencing attitudes and intentions. Our study analyses the relationship between two primary benefits: the household-level financial gain and the broader environmental advantage. Focusing on the German homeowners’ intention to adopt Rooftop Photovoltaic Systems, Energy Efficient Appliances, and Green Electricity Tariffs, we employ Partial Least Squares Structural Equation Modeling to demonstrate that the adoption intention of the LCTs is underpinned by the Theory of Planned Behaviour. Our research also highlights the pivotal role of perceived benefits in shaping attitudes and intentions. Attitudes towards the LCTs are more strongly related to product-specific benefits than affective constructs. In terms of evaluative benefits, environmental benefits exhibit a higher positive association with attitude formation compared to financial benefits. However, this relationship switches as homeowners move through the decision process with the financial benefits of selected LCTs having a consistently higher association with adoption intention. At the same time, financial benefits also positively affect attitudes. Observing this trend across both low- and high-cost LCTs, we recommend that policymakers and businesses enhance homeowners’ awareness of the personal advantages and implement strategies to make these financial benefits more tangible and accessible.

Co-adoption pathways toward a low-carbon energy system

Low-carbon technology adoption is an essential element of energy transitions toward net-zero emissions around the world. To exploit the full potential of low-carbon technologies, households should ideally co-adopt multiple low-carbon technologies. Whereas previous research primarily investigated predictors of single-technology adoption in isolation, here we focus on the co-adoption of multiple low-carbon technologies, including solar photovoltaics, stationary batteries, heat pumps, and electric vehicles, to examine the interconnections between adoption decisions and the potential of certain technologies to serve as "entry points" for the co-adoption of multiple low-carbon technologies. Based on a sample of 1967 homeowners, we identified unique demographic and psychological variables associated with co-adoption. We moreover observed specific co-adoption patterns across time in that the adoption of one technology increased the likelihood of adopting another technology. This effect, however, was primarily driven by co-adoption in close temporal proximity, pointing to opportunities for targeted policies that support technology bundles.

Sustainable Agriculture in the Face of Climate Change: Exploring Farmers’ Risk Perception, Low-Carbon Technology Adoption, and Productivity in the Guanzhong Plain of China

Agriculture is a significant contributor to global greenhouse gas emissions, and reducing carbon emissions in this sector is essential for mitigating global warming. To achieve China’s targets of carbon peak by 2030 and carbon neutrality by 2060, promoting low-carbon agricultural technology (LCAT) is fundamental. This study examines the impact of farmers’ risk perception on LCAT adoption behavior and its productivity effects with the Ordered Probit regression method, using micro survey data from 531 farmers in Shaanxi Province, China. The results show that farmers with stronger risk perceptions were more likely to adopt LCAT, based on their loss aversion characteristics. Additionally, farmers’ perceptions of yield, market, and climate risks positively influence the adoption of LCAT, with market risk perception having the strongest effect. Adopting LCAT has significant production and spillover effects, improving the output rate of farmers’ operating farmland and neighboring plots by 2.4% and 1.2%, respectively, for each additional measure adopted. This study contributes to the perception and loss aversion literature by examining farmers’ adoption of low-carbon agricultural practices. This study sheds light on the importance of risk perception in the adoption of sustainable agricultural practices and can inform policies aimed at promoting the adoption of LCAT for achieving sustainable agriculture and mitigating climate change, highlighting the crucial role of sustainable environmental management in the agricultural sector.

Classification of Methods to Reduce the Carbon Footprint

The ever-increasing amount of greenhouse gases in the atmosphere has an adverse impact on the environment, causing global warming and related adverse effects. One of the most important greenhouse gases is carbon dioxide, the production of which is perceived as a carbon footprint. The carbon footprint is intended to be a tool for appropriate emission reductions and verification, so there is a great need for its standardization at the global level. In carrying out basic research and after working out the advantages and disadvantages, many methods of decarbonization have been identified. The author believes that by simplifying the classification of carbon footprint reduction approaches into appropriate classes, a clear tool guiding companies in decarbonizing their operations will be created so that the negative impacts of global warming on the Earth are averted as quickly as possible. The aim is, therefore, to identify similarities and differences in methods, which will lead to the identification of new main groups of approaches. Within the framework of the evaluation and after making a comparison according to selected aspects of intensity, seven basic approaches have emerged, thus achieving greater clarity, usability, and better orientation for companies on the issue of reducing their carbon footprint. The results are illustrated at the end of the paper. The least challenging method for low-carbon technology adoption is the promotion of education and dissemination of climate impact awareness. Conversely, the most challenging method appears to be the construction of new green buildings. Keywords: Advantages; Carbon footprint; Classification of ways; Comparison; Disadvantages.

ENERGY TRANSITIONS IN NIGERIA: THE ROLE OF POLICIES FOR THE ADOPTION OF LOW-CARBON TECHNOLOGIES AND SYSTEM INTEGRATION

This study underscores the role of Policies for adopting low-carbon technologies and system integration in Nigerias energy transition. Although the nation has abundant energy resources, a sizeable section of its people lacks access to power, creating severe problems with energy accessibility. To address this problem, the government has implemented several programmes and plans. However, the high initial costs of low-carbon technology, particularly in rural regions, have hampered their development. The study emphasises the importance of a user-centred approach, contextualisation of technology, consideration of social norms, and the role of intermediaries in facilitating the adoption and use of low-carbon technologies. It also highlights how policy interventions can either accelerate or delay the adoption of low-carbon technologies. The study recommends that policymakers and technology developers adopt a holistic approach by considering various issues in policy formulation concerning the transition to low-carbon technologies. KEYWORDS: Energy Transitions, Low-Carbon Technologies, System Integration, Policies, Plans, and Nigeria

An Integrated Model to Conduct Multi-Criteria Technology Assessments: The Case of Electric Vehicle Batteries.

The large-scale adoption of low-carbon technologies can result in trade-offs between technical, socio-economic, and environmental aspects. To assess such trade-offs, discipline-specific models typically used in isolation need to be integrated to support decisions. Integrated modeling approaches, however, usually remain at the conceptual level, and operationalization efforts are lacking. Here, we propose an integrated model and framework to guide the assessment and engineering of technical, socio-economic, and environmental aspects of low-carbon technologies. The framework was tested with a case study of design strategies aimed to improve the material sustainability of electric vehicle batteries. The integrated model assesses the trade-offs between the costs, emissions, material criticality, and energy density of 20,736 unique material design options. The results show clear conflicts between energy density and the other indicators: i.e., energy density is reduced by more than 20% when the costs, emissions, or material criticality objectives are optimized. Finding optimal battery designs that balance between these objectives remains difficult but is essential to establishing a sustainable battery system. The results exemplify how the integrated model can be used as a decision support tool for researchers, companies, and policy makers to optimize low-carbon technology designs from various perspectives.

Mapping social capital across Wales (UK) using secondary data and spatial analysis

Social capital, a powerful community resource based on trust, relationships, norms, culture, values, networks and belonging, could shape the acceptance, cooperation, and involvement of citizens towards new policies or interventions. In past, connections of social capital have been studied in relation to human health, wellbeing, social and economic development. More recently, social capital has been studied with respect to human resilience and adaptation to climate change. We argue that social capital could also play a vital role in our efforts to reduce carbon footprint through behaviour change, a shift on shared local renewable energy resources, and adoption of low carbon technologies. In Wales (UK) there is no national scale dataset, reflecting its social capital landscape, that could be used for designing the right policies/interventions in this context, based on an expected level of trust, cooperation, and support within the communities. This paper is an effort to fill this data gap using secondary datasets. Firstly, a literature review is carried out to identify the indicators of social capital (cognitive and participatory). Secondary datasets have then been identified and acquired. Geospatial analysis has been carried out to produce the criterion maps for various indicators of social capital. Finally, Analytical Hierarchy Process is applied to generate a social capital map of Wales combining these indicators together. For validation of the produced data, social capital’s known correlations were tested with crime rates, income level and multiple deprivations.

Socioeconomic Inequality in Low-Carbon Technology Adoption

Socioeconomic Inequality in Low-Carbon Technology Adoption

Modulating redox properties of solid-state ion-conducting materials using microwave irradiation.

The industrial adoption of low-carbon technologies and renewable electricity requires novel tools for electrifying unitary steps and efficient energy storage, such as the catalytic synthesis of valuable chemical carriers. The recently-discovered use of microwaves as an effective reducing agent of solid materials provides a novel framework to improve this chemical-conversion route, thanks to promoting oxygen-vacancy formation and O2-surface exchange at low temperatures. However, many efforts are still required to boost the redox properties and process efficiency. Here, we scrutinise the dynamics and the physicochemical dependencies governing microwave-induced redox transformations on solid-state ion-conducting materials. The reduction is triggered upon a material-dependent induction temperature, leading to a characteristically abrupt rise in electric conductivity. This work reveals that the released O2 yield strongly depends on the material's composition and can be tuned by controlling the gas-environment composition and the intensity of the microwave power. The reduction effect prevails at the grain surface level and, thus, amplifies for fine-grained materials, and this is ascribed to limitations in oxygen-vacancy diffusion across the grain compared to a microwave-enhanced surface evacuation. The precise cyclability and stability of the redox process will enable multiple applications like gas depuration, energy storage, or hydrogen generation in several industrial applications.

An evolutionary game model for low-carbon technology adoption by rival manufacturers

An evolutionary game model for low-carbon technology adoption by rival manufacturers

Socio-economic factors influencing the adoption of low carbon technologies under rice production systems in China.

Rice (Oryza sativa L.) production, such as farmers' livelihood and the soil quality, has been identified to be strong influenced by climate change in China. However, the benefits of low carbon technologies (LCTs) are still debatable in rice production for farmers, which have been identified to tackle agricultural challenges. The choice of potential LCTs relevant to the case study is based on a literature review of previous empirical studies. Thus, the objectives of the study were to (1) investigate the public perception and preferences of LCTs in rice production of China, and (2) analyze the influences of the factors on farmer's decision in adopting LCTs in rice production. There were 555 farmer surveys from eight representative rice production counties in HP province of southern China, both the Poisson estimators and multivariate probit (MVP) approach were applied in the study. Our results show that water-saving irrigation, integrated pest management techniques and planting green manure crops in winter season were the three major LCTs adapted by farmers in rice production. The intensity and probability of LCTs adoptions were influenced by the main factors including farmers' education level, climate change awareness, machinery ownership, technical support and subsidies. There is a significant correlation among the LCTs, and the adoption of the technologies is interdependent, depicting either complementarities or substitutabilities between the practices. This study suggests that policies enhance the integration of LCTs would be central to farmers' knowledge, environmental concerns, technical service and financial support in rice production systems in China.

Carbon Pricing in the US: Examining State-Level Policy Support and Federal Resistance

Carbon pricing is a key policy instrument used to steer markets towards the adoption of low-carbon technologies. In the last two decades, several carbon pricing policies have been implemented or debated at the state and federal levels in the US. The Regional Greenhouse Gas Initiative and the California cap-and-trade policy are the two regional policies operational today. While there is no federal policy operational today, several carbon pricing proposals have been introduced in Congress in the last decade. Using the literature on interest group politics and policy entrepreneurship, this article examines the carbon pricing policies at the subnational and federal levels in the US. First, the article explores the evolution of two main regional carbon pricing policies, the Regional Greenhouse Gas Initiative and California cap-and-trade, to identify how interest groups and policy entrepreneurs shaped the design and implementation of the respective policies. Second, the article details the federal carbon pricing policy proposals and bills discussed in the last decade. Third, it examines the factors that limit the prospects of realizing an ambitious federal carbon price for pursuing deep decarbonization of the US economy. The article finds that federal carbon pricing in the US suffers from the lack of any natural and/or consistent constituency to support it through policy development, legislation, and implementation. While interest group politics have been mitigated by good policy entrepreneurship at the subnational level, the lack of policy entrepreneurship and the changing positions of competing interest groups have kept a federal carbon pricing policy from becoming a reality.

Timing Decision of Low-Carbon Technology Investment Adoption by High Energy Consuming Enterprises under Carbon Trading and Subsidies.

Although government subsidies provide some financial support for firms to invest in low-carbon technologies, carbon price fluctuations bring greater uncertainty risks to firms' investment. The paper constructs a real option model to analyze the timing of low-carbon technology adoption between upstream dominant high energy consuming firms and downstream retailers in case of collaborative decision-making and Stackelberg game, and a numerical simulation is conducted to analyze factors affecting the timing for low-carbon investment. We find that the proportion of cost subsidies, carbon price volatility, carbon emission reduction rate, and cost-sharing ratio will affect firms to choose the optimal investment opportunity.

Electrification of Online Ride-Hailing Vehicles in China: Intention Modelling and Market Prediction

Significant negative impact caused by climate changes, such as economy and life losses, has been experienced globally in recent years, which has called for imminent development and adoption of low carbon technologies in order to mitigate the impact. In 2020, the Chinese government outlined the ‘Dual Carbon’ Goal where its carbon emission will peak before 2030 whilst China will become ‘Carbon Neutral’ before 2060. In China, the amount of carbon emissions from the transportation industry stands in second place and it is predicted that the carbon emission of China’s automobile industry will reach between 21.5 and 30 billion tons in 2030. Actions should be taken as quickly as possible to facilitate the transition from traditional fossil fuel vehicles to low carbon vehicles such as electric vehicles in order to reduce carbon emissions effectively. Based on the questionnaire that is designed to survey the electrification of online ride-hailing vehicles, this paper first establishes a consumer purchase intention model according to the perceived value theory. By evaluating six aspects including functional value, emotional value, social value, functional risk, financial risk and physical and mental risk, the regression model of the consumer purchase intention for electric vehicles is built. Subsequently, the average operating models for petroleum fuel vehicles, hybrid vehicles and electric vehicles are established, and on top of this, a fossil fuel price model can be derived. This price model can identify from which price it will be advantageous to use electric vehicles to run an online ride-hailing service. Moreover, a multi-agent model is established to illustrate the spread of electric vehicles in the online ride-hailing sector and the private car sector, which is used to predict the trend of the EV market development in China from 2020 to 2040.

Patterns and determinants of carbon emission flows along the Belt and Road from 2005 to 2030

The Belt and Road Initiative (BRI) has promoted economic growth of participating countries while giving rise to profound environmental consequences. To steer the BRI towards a low-carbon and green development, it is necessary to analyze past trajectories and future trends of BRI's CO2 emissions. To this end, we assess the patterns and determinants of emission flows along the BRI during 2005–2030 using the multi-region structural decomposition analysis technique. For the period 2005–2015, we show that intermediates export of the BRI embodied more CO2 emissions than final goods export. The significant technological improvement only partly offset the emission growth stemming from the deteriorated cross-border production structure and the surging final demand of the BRI in the past. For the period 2015–2030, our prospective analysis indicates that emissions embodied in exports of participating countries increase by over 20% in the reference scenario where historical development patterns of the BRI continue. The rise might be even higher if the initiative ends. On the contrary, enhancing diffusion and adoption of low-carbon technologies and promoting green trade within the BRI show substantial emission mitigation potential. Our empirical results reveal directions and priorities for policymaking in the pursuit of a green BRI.

The impact of extreme weather on peak electricity demand from homes heated by air source heat pumps

ABSTRACT Ireland has a renewable heat sectoral target of 600,000 heat pumps by 2030 with currently just 44,000 installed. Such a high heat pump target will have a significant effect on electricity demand and on the management and operation of the electricity grid. Many studies rely on synthetic data to estimate the impact of the adoption of low carbon technologies. This paper explores residential electricity demand using an innovative dataset from a field trial of deeply retrofitted homes heated by air source heat pumps. We estimate the after diversity maximum demand during a period of extreme weather, comparing it to a typical winter day. We assess which statistical distributions best model the electricity demand per home heated by an air source heat pump. The after diversity max demand is 3.84 kW compared to 2.33 kW, while a Gamma distribution best models average coincident electricity demand for the homes.