Determining the influencing factors of consumers' attitude toward renewable energy adoption in developing countries: a roadmap toward environmental sustainability and green energy technologies

Exploring the impact of renewable energy, green taxes and trade openness on carbon neutrality: New insights from BRICS countries

The world faces two significant challenges: promoting sustainable economic growth and reaching carbon neutrality. In BRICS countries, these challenges are shaped by renewable energy, green taxes, and trade openness. These countries were selected for their strategic location and the abundance of relevant data collected over the period of 1990-2021, providing a distinctive window into the energy and economic dynamics of the area. The link between renewable energy consumption, green taxes, trade openness, and natural resources and their effects on carbon emissions in BRICS countries is examined in this study using the Fully Modified Ordinary Least Square Method (FMOLS) estimator and the Drisc Kraay estimator for the robustness test. The findings indicate that using renewable energy and green taxes primarily contribute to reducing emissions, particularly at higher emissions levels. The study reveals that various factors, namely financial globalization, trade openness, efficient resource management, and population growth, substantially impact carbon neutrality. Population growth positively impacts carbon neutrality, while using renewable energy sources mitigates it. Furthermore, the empirical findings show a statistically significant positive association between financial globalization, efficient resource management, and carbon neutrality in BRICS nations. Therefore, it is necessary to implement an integrated ecological governance strategy to control and direct financial resources towards sustainable development and green energy.

Mitigating environmental pollution in China: Unlocking the potential for high-quality innovation

The nexus between environmental pollution (EP) and technological innovation is crucial for achieving sustainable development. However, existing literature has paid less attention to the new form of high-quality innovation (HI) in environmental management. This paper uses panel data from 31 Chinese provinces from 2008 to 2020, employing the two-stage least squares method to investigate the relationship between HI and EP. The empirical results reveal that HI can effectively reduce the EP, which holds after multiple robustness tests, and this effect is more obvious in southern China. Meanwhile, HI drives clean and efficient energy transition and decreases EP. Moreover, increased environmental regulation weakens the influence of HI on EP. The major contributions of this study are constructing an HI index including innovation, human capital, and government support and examining its influence on EP in China. The findings encourage government to implement policies of innovation-driven transformation, energy conservation and emissions reduction.

Mechanism analysis of rural residents' participation in green energy transition: A community-level case study in Nanjing, China

Achieving a transition to green energy requires the government to adopt new policies on green industrial products and energy. Throughout this process, rural residents often face various challenges due to economic and other factors. However, some rural residents are motivated to participate in this transition due to the economic benefits of energy usage and rooftop photovoltaic (PV). This study takes a rural community in the outskirts of Nanjing, China, as an example and applies Granovetter's embeddedness theory and a threshold model to analyze the factors influencing rural residents' engagement in a fair transition to green energy. Research hypotheses are proposed accordingly. The results indicate that rural residents are influenced by multiple factors in the adoption process of rooftop PV projects, primarily encompassing economic and trust-related aspects. From an economic perspective, rural residents evaluate the viability of rooftop PV systems by considering the marketing strategies employed by PV enterprises and the governmental pressure to reduce carbon emissions. They make rational calculations to determine the return on investment, and only when the economic threshold is surpassed will they reach the anticipated level of participation. From the perspective of trust, rural residents' participation in rooftop PV projects is also influenced by trust factors. The level of trust that rural residents have in rooftop PV enterprises, governments, and community organizations plays an important role in their willingness to participate in the green energy transition. Based on these findings, the research paper concludes that local government should continue providing tailored public information and services to facilitate the progress of rooftop PV projects.

Energy markets restructure beyond 2022 and its implications on Qatar LNG sales strategy: Business forecasting and trend analysis

The emergence of new suppliers and energy resources has reshaped the energy market in terms of contractual structures and pricing systems. The market shifts were accelerated in response to the latest Russian-Ukraine crisis, impacting natural gas supply chains from financing projects to contracting volumes. The increased demand for liquified natural gas volumes intensified the need to switch from long-term oil-indexed contracts to short-term gas-indexed contracts. Those shifts were anticipated to influence the selling strategies for the expected added 49 MTPA of Qatari LNG, wherein increasing the share of spot selling would be reflected in higher economic performance. This study used forecasted prices to investigate potential Qatari LNG selling strategies. Initially, projections of the most dominant pricing systems used for pricing Qatari LNG (i.e., brent, Henry Hub, Title Transfer Facility, and Japan Korea Marker) were estimated between 2023 and 2040. While Qatar has been relying on long-term oil-indexed contracts, the second step estimated annual LNG revenues under different combinations of selling strategies (i.e., long-term and spot sales). Finally, the influence of varying brent slopes on the estimated revenues was measured. Due to data limitations and non-stationarity, the double exponential smoothing model was selected among the different tested models. Considering current market dynamics, forecasts of the double exponential smoothing model showed an upward price trend until 2040. An annual average increase of 1.24% for the studied pricing systems was reported. Reducing the share of long-term brent-indexed contracts to 70% and dedicating the remaining 30% of volumes to spot sales yielded the highest premiums for revenue estimates. An average annual revenue of $62 bn was reported for the 70/30 strategy, around 6% higher than the 100% brent-indexed contracts strategy. The findings revealed that diversifying the selling approach and introducing spot sales can enhance revenues. From the buyers' perspective, the outcomes support policymakers in understanding the implications of escalated prices driven by a lack of liquidity investments.

Unlocking Urbanization: The symbiotic relationship between inclusive finance and urban development in China

The emergence and development of inclusive finance has made it possible for more economic entities to have easy access to a wider selection of financial services. This shift has significantly addressed the financial challenges inherent in the process of urbanization, making it a driver of the process of urban development. Therefore, this paper provides empirical evidence on the relationship between financial inclusion development and urbanization construction in China using provincial data and a panel-VAR model. The results show that: (1) There is a significant co-integration relationship among inclusive finance, urbanization, government support, and real estate development. (2) Inclusive finance has a long-term positive impact effect on urbanization. (3) Population urbanization has a positive impact on inclusive finance, but income urbanization has a negative impact on inclusive finance. To effectively promote the development of inclusive finance and urbanization, the following measures are of utmost importance: Firstly, while accelerating urbanization construction, it is necessary to expand and enhance the coverage of financial services. This will ensure that multiple regions can benefit from financial services. Secondly, to meet the diverse needs of different regions, more targeted financial products should be developed, making full use of the advantages of inclusive finance. Lastly, the government should strengthen its supervision of financial institutions and reduce the risks associated with inclusive finance, thereby ensuring a positive interaction between inclusive finance and urbanization development.

Quantifying digital economy and green initiatives for carbon neutrality targets: a Kilian bias-adjusted bootstrap model evaluation of China economy

Digitalization has emerged as a new hope for low-carbon sustainable economic growth after its successful trial during the COVID-19 measures. Therefore, both developed and developing economies focus on digitalization to cope with carbon neutrality targets. Thus, this study attempted to generate a meaningful relationship between the digital economy and green energy, innovation, and environmental tax policy to capture the role of factors in acquiring carbon neutrality. For the abovementioned objectives, modern econometric methods, such as the Kilian bias-adjusted bootstrap, were adopted to evaluate the Chinese dataset between 1990 and 2021. The results indicate that the study factors play a significant role in acquiring carbon neutrality in the long-term Chinese economy. Furthermore, quantile autoregressive distributed lag model (QARDL) indicates that all the factors influence carbon neutrality in various quantiles. Consequently, the digital economy, green energy and innovation, and environmental taxes significantly assist in attaining carbon neutrality in the long term, and the ecological Kuznets curve prevails in the economy. Therefore, radical and wide-ranging policy implications are required in many areas including environmental restrictions, digital economy promotion, green and sustainable technologies, and clean energy sources.

Reducing carbon emissions through green renewal: insights from residential energy consumption in Chinese urban inventory districts from an evidence-based decision-making perspective

The green regeneration of urban inventory is now much more viewed as a strategy to create “green finance”, in contrast to the urban renewal movement that was started in the 1950s and focused on the physical renovation of the built environment. This study draws on evidence-based theory to examine the spatial forms of residential communities in 36 urban districts of China. From a morphological perspective, it summarizes the spatial forms of residential environments at the level of buildings and microdistricts. The results indicate that in urban areas, the aspect ratio and shape coefficient of residential buildings have a significant impact on household energy consumption, and an appropriate increase in building density will help reduce carbon emissions. In conclusion, this study emphasizes the role of carbon reduction as a kind of “green finance” and provides a reference for subsequent urban renewal decisions in combination with China’s actual cases.

Towards facing uncertainties in biofuel supply chain networks: a systematic literature review

Biofuel supply chains (BSCs) face diverse uncertainties that pose serious challenges. This has led to an expanding body of research focused on studying these challenges. Hence, there is a growing need for a comprehensive review that summarizes the current studies, identifies their limitations, and provides essential advancements to support scholars in the field. To overcome these limitations, this research aims to provide insights into managing uncertainties in BSCs. The review utilizes the Systematic Reviews and Meta-Analyses (PRISMA) method, identifying 205 papers for analysis. This study encompasses three key tasks: first, it analyses the general information of the shortlisted papers. Second, it discusses existing methodologies and their limitations in addressing uncertainties. Lastly, it identifies critical research gaps and potential future directions. One notable gap involves the underutilization of machine learning techniques, which show potential for risk identification, resilient planning, demand prediction, and parameter estimations in BSCs but have received limited attention. Another area for investigation is the potential of agent-based simulation, which can contribute to analysing resilient policies, evaluating resilience, predicting parameters, and assessing the impact of emerging technologies on BSC resilience in the twenty-first century. Additionally, the study identifies the omission of various realistic assumptions, such as backward flow, lateral transshipments, and ripple effects in BSC. This study highlights the complexity of managing uncertainties in BSCs and emphasizes the need for further research and attention. It contributes to policymakers' understanding of uncertain sources and suitable approaches while inspiring researchers to address limitations and generate breakthrough ideas in managing BSC uncertainties.

Identifying the electricity-saving driving behaviors of electric bus based on trip-level electricity consumption: a machine learning approach

Electric buses (EBs) are gaining popularity worldwide as a more sustainable and eco-friendly alternative to diesel buses (DBs). Electricity-saving driving plays a crucial role in minimizing an EB's energy consumption, subsequently leading to an extended driving range. This study proposes a machine learning-based framework for identifying electricity-saving EB driving behaviors during various driving stages, including running on road segments, entering bus stops/intersections, and exiting bus stops/intersections. The proposed random forest (RF) model effectively evaluates the energy consumption level using EB drivers' historical driving data under different scenarios. Specifically, the electricity consumption factor (ECF), as the evaluation index, is divided into three categories to determine the implicit relationship between driving behavior and energy consumption. The results indicate that the classification accuracy of RF models surpasses 90%, which highlights the effectiveness in accurately identifying energy-efficient EB driving behaviors. In addition, the Shapley additive explanations (SHAP) and partial dependency plots (PDPs) are utilized to visualize and interpret the results of RF models. A speed interval of 30-40 km/h is identified as the most energy-efficient range for EB running on a road segment. Findings from this study can be applied to targeted optimization of electricity-saving driving strategies in different driving scenarios to improve the overall efficiency and sustainability of the transportation system.

A web-based system to determine risk of investment in international rail construction projects

Manual evaluation of investment risk make results and solutions are not timely. The objective of the study is to explore intelligent risk data collecting and risk early warning of international rail construction. First, this study has identified risk variables by content mining. Second, risk thresholds are calculated by the quantile method based on data from 2010 to A.D. 2019. Third, this study has developed risk early warning system by the gray system theory model, the matter-element extension method and the entropy weight method. Fourth, the risk early warning system is verified using Nigeria coastal railway project in Abuja. This study found that: (1) the framework of the developed risk warning system contains a software and hardware infrastructure layer, a data collection layer, an application support layer, and an application layer. (2) 37 investment risk variables are recognized; (3) 12 risk variables thresholds intervals are not equally divided between 0 and 1, the others are evenly distributed; (4) based on the application of Nigeria coastal railway project in Abuja, the system verification results are consistent with real situations, which is shown that risk early warning system is reasonable and feasible. These findings offer a good reference for intelligent risk management.

Retirement Planning and Financial Anxiety among Nigerian Civil Servants: Insights from Social Comparison Theory

The psychological implication of retirement is underemphasised. This study examined the relationship between proactive personality, social comparison, and retirement anxiety among Nigerian civil servants. The study is a cross-sectional design, using proactive personality, social comparison orientation, and Nigerian pre-retirement anxiety scales. Five hundred and eight staff members in government-owned tertiary institutions with five years or less to go until retirement, and at a mean age of 57.47 (SD = 3.02), were surveyed. The study established that a proactive personality negatively predicted retirement anxiety and that civil servants engage in diverse forms of intrapreneurship/entrepreneurship to augment their savings. The study also revealed that social comparison (opinion) mediated the relationship between proactive personality and retirement anxiety (financial preparedness and social alienation). In addition, the study found that social comparison (opinion and ability) mediated the relationship between proactive personality and retirement anxiety (financial preparedness) in a sequential order. The findings suggest that retirees in Nigeria face complex challenges, including financial unpreparedness, social alienation, and uncertainty. The study highlights the importance of understanding the relationship between personality traits, social comparison, and retirement anxiety in order to develop effective interventions and policies that support retirees in Nigeria.