Influencing factors of consumers' buying intention of solar energy: a structural equation modeling approach

Does green investment affect environment pollution: Evidence from asymmetric ARDL approach?

Pollution in the environment is today the biggest issue facing the globe and the main factor in the development of many fatal diseases. The main objective of the study to investigate green investments, economic growth and financial development on environmental pollution in the G-7 countries. This study used annual penal data from 1997 to 2021. The panel NARDL (Non-linear autoregressive distributed lag) results affirm that the positive change of green investment and negative shock in green investment have a significant and positive association with environment pollution in G-7 nations. Our findings provide more evidence for the long-term asymmetry between financial development and environmental performance. However, the findings confirm that a positive modification in financial development has a positive and significant effect on environment pollution. Whereas negative shock in financial development is negative and insignificant relationship with environment pollution. Moreover, the outcomes of the study reveal that both positive shock in gross domestic product growth and negative shock of economic growth have a significant and positive link with environment pollution in G-7 countries. According to the findings, by lowering carbon dioxide emissions, green investments reduced environmental pollution in the G-7 nations over the long and short term. Moreover, it is an innovative research effort that provides light on the connection between green investments, financial development, and the environment while making mention to the EKC in G-7 countries. After all these, our recommendation is to increases green investment expenditures to reduce environmental pollution in the G-7 nations based on our findings. Additionally, one important way for the nation to achieve its sustainable development goals is to improve advancements in the financial sector.

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.

Smart city pilots, marketization processes, and substantive green innovation: A quasi-natural experiment from China

The world's major economies are striving to control carbon emissions and avoid irreversible impacts on the natural environment. Therefore, innovative green technologies are crucial for both government departments and the private sector as an important way to address carbon emissions. This study aims to investigate the link between the government's smart city construction and corporate green innovation and optimize the policy guidelines that drive green innovation in enterprises. This study analyzes 6,104 panels of Chinese listed companies from 2007-2019. An approach called the Differences-in-Differences model was applied to evaluate hypotheses. The empirical results suggest that smart city pilots drove substantial green innovation in businesses. The marketization process has a moderating effect on the impact of smart city pilots on substantive green innovation in enterprises. Moreover, marketization process has a threshold effect in smart city pilots influencing the substantive green innovation of enterprises, and the effect of smart city drivers influencing the substantive green innovation of enterprises increases significantly when regional marketization process reaches a certain level. The findings of this study provide valuable guidance for policy designers to promote corporate green innovation at both the hardware facility level and the market system level of cities when developing policies related to green innovation.

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.

Assessing eco-label knowledge and sustainable consumption behavior in energy sector of Pakistan: an environmental sustainability paradigm

The energy needs of Pakistan have increased many folds in recent years due to improved lifestyle, ever-increasing population, and economic development. Though the government and private sectors are considering efficient energy resources to overcome energy scarcity in the country, studies focusing on assessing consumers' sustainable consumption behavior in the form of energy-saving home appliances are limited in the country. This study aims to address this research gap and also contribute by augmenting the theoretical mechanism of the theory of planned behavior by including three unique dimensions (eco-label knowledge, attitude toward environment, and customer green trust) to comprehensively analyze sustainable consumption behavior in the Pakistani context. An analysis is performed on survey data of 631 consumers in the four largest cities of Pakistan: Karachi, Lahore, Faisalabad, and Islamabad. For the purpose of evaluating formulated hypotheses, the structural equation modeling approach is employed. Empirical findings suggest that eco-label knowledge positively and significantly influences attitude toward environment and consumer green trust. Similarly, attitude toward environment and consumer green trust has a positive and significant influence on purchase intention. Moreover, a significant positive relationship exists between consumer green trust and purchase intention. The research outcomes further disclose that purchase intention positively and significantly influence paying attention to environmental labels. These findings contribute to the literature on sustainable consumption behavior and provide academics and practitioners with future directions to transform social norms, raise consumers' awareness, and redesign policy frameworks through integrative and consistent efforts.

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

The energy sector is a crucial pillar of the economic development of every nation. In developing countries, renewable energy deployment is scarce; consequently, the government and private sectors are exploring efficient energy resources. This research aims to scrutinize the linkages among value orientation, utilitarian benefits, collectivism, the reason for adoption, attitude toward renewable energy (RE), and adoption intention in the renewable energy context. The study analyzes survey data from 359 Pakistani consumers using solar panels for households. An approach called structural equation modeling is applied to evaluate hypotheses. Empirical findings suggest that value orientation positively and significantly influences the reason for the adoption of RE and attitude toward RE. Similarly, the utilitarian benefit positively and substantially affects attitude toward RE. Moreover, collectivism and reason for adoption are substantially and favorably related to attitude toward RE. The study's findings also show that customer intentions to use renewable energy are favorably and substantially influenced by RE attitudes. The research has contributed to the enhancement of future avenues for scholars and professionals are provided by the literature on renewable practice.

Relating green information acquisition, absorptive capacity, institutional pressure, and firm performance: an environmentally sustainable perspective

Numerous Chinese manufacturing organizations are grappling with the challenge of absorbing and using green information acquisition across the supply chain to achieve excellent firm performance. Utilizing the organizational learning theory's indirect stream of research, we address this research gap by developing a moderated-mediation framework to investigate the impact of green information acquisition and institutional pressure on a firm's performance. Hypotheses are evaluated by taking a sample of 567 manufacturing enterprises in China. Structural equation modeling (SEM) has been applied to analyze and investigate the proposed hypotheses. Empirical results indicate that absorptive capacity significantly mediates the relationship between green information acquisition and firm performance. In a similar vein, institutional pressure significantly moderates the relationship between green information acquisition and firm performance. Study findings have essential managerial recommendations for Chinese manufacturing enterprises, proposing that they considerably enhance their absorptive capacity and continuously monitor institutional pressure to reap the advantages of green information acquisition on firm performance.