Electric vehicles' choice behaviour: An emerging market scenario.

Worldwide, the adoption of electric automobiles is gaining momentum, owing to a steady rise in customers' sustainability consciousness. So far, electric vehicle-related studies have investigated consumer motives, attitudes, and intentions toward adoption. However, empirical research on enablers and inhibitors of electric vehicle choice behaviour has not been fully explored, particularly in an emerging market context, (e.g., India). The present study employed a judicious mix of three notable theoretical lenses of dual-factor theory, innovation resistance theory, and the stimulus-organism-response model to empirically scrutinize electric vehicle adoption enablers and inhibitors by analysing data collected from 391 young Indian sustainability-oriented electric vehicle users. The sample was gathered via the purposive sampling method, and the data was analysed employing structural equation and PROCESS macro modelling. The research posits that consumer sustainability consciousness (CSC) is a stimulus with a positive influence on enablers (e.g., personal motives, social motives, and incentive policy) as well as inhibitors (e.g., usage, value, and risk barriers). Additionally, product involvement and perceived marketplace influence significantly moderate the relationship between choice behaviour and facilitators and inhibitors. The research offers a few useful strategic decision-making insights for electric vehicle manufacturers, green marketers, and policymakers from emerging markets.

The impact of Chinese airport infrastructure on airline pollutant emissions: A hybrid stochastic-neural network approach based on utility functions.

With China being the world's largest emitter of greenhouse gases and its aviation sector burgeoning, the environmental performance of Chinese airlines has global significance. Amidst rising demands for eco-friendly practices from both customers and regulators, the interplay between airport infrastructure and environmental performance becomes pivotal. This research offers an innovative methodology to gauge the environmental performance of Chinese airlines, emphasizing the distance traveled between airports using weighted additive utility functions. Leveraging neural networks, the study investigates the impact of various airport infrastructural characteristics on environmental performance. Noteworthy findings indicate that ground control measures, automatic information services at origin airports, surface concrete on runways at both ends, and a centerline lighting system in destination airports positively influence environmental performance. In contrast, longer and wider runways at origin airports, increased distances to control towers, and asphalt runways at destination airports adversely affect it. These insights not only underscore the importance of strategic infrastructure enhancements for reducing carbon footprints but also hold profound policy implications. As global climate change remains at the forefront, fostering sustainable airport infrastructure in China can significantly contribute to worldwide mitigation efforts.

Uncovering Bitcoin's electricity consumption relationships with volatility and price: Environmental Repercussions.

Bitcoin, a global financial asset, surpassed one trillion USD in November 2021, but its environmental impact may cause a 2 °C temperature rise by 2050. Using causal and connectedness analysis, we uncover non-linear relationships between bitcoin's energy consumption, price, and the Crypto Volatility Index. This study uses 1458 daily observations from several databases from March 31, 2019, to March 30, 2023. The phenomenon was analyzed using the theory of production and value investing theory. While the relationship between bitcoin-based electricity consumption and crypto market volatility is bidirectional, Granger causality tests reveal that bitcoin prices Granger-cause electricity consumption, but the converse is not true. Regarding Diebold-Yilmaz connectedness, the price of bitcoin acts as a net contributor, while bitcoin-based electricity consumption and crypto market volatility act as net receivers of spillover from bitcoin price. Our findings contrast with the traditional theory of production, where cost is supposed to determine price, and we show that some bitcoin miners continue operating according to the value investing theory despite suffering financial losses. Limited discussions around bitcoin pricing and its significant expense-that is bitcoin's electricity consumption-indicate the need to explore this relationship. Policymakers, green investors, and others may find the results relevant to building an efficient, environmentally friendly framework and creating much-required innovative regulations.

Time-varying impacts of green credit on carbon productivity in China: New evidence from a non-parametric panel data model.

In the context of global climate change threatening human survival, and in a post-pandemic era that advocates for a global green and low-carbon economic recovery, conducting an in-depth analysis to assess whether green finance can effectively support low-carbon economic development from a dynamic perspective is crucial. Unlike existing research, which focuses solely on the average effects of green credit (GC) on carbon productivity (CP), we introduce a non-parametric panel data model to investigate GC's impact on CP across 30 provinces in China from 2003 to 2021, verifying a significant time-varying effect. Specifically, during the first phase (2003-2008), GC negatively impacted CP. In the second phase (2009-2014), this negative influence gradually diminished and transformed into a positive effect. In the third phase (2015-2021), GC continued to positively influence CP, although this effect became insignificant during the pandemic. Further subgroup analysis reveals that in the regions with low environmental regulations, GC did not significantly boost CP throughout the sample period. In contrast, in the regions with high environmental regulations, GC's positive effect persisted in the mid to late stages of the sample period. Additionally, compared to the regions with low levels of marketization, the impact of GC on CP was more pronounced in highly marketized regions. This indicates that the promoting effect of GC on CP depends on strong support from environmental regulations and well-functioning market mechanisms. By adopting a non-parametric approach, this study reveals variations in the impact of GC on CP across different stages and under the influence of the pandemic shock, offering new insights into the relationship between GC and China's CP.

Impact of Climate Policy Uncertainty (CPU) and global Energy Uncertainty (EU) news on U.S. sectors: The moderating role of CPU on the EU and U.S. sectoral stock nexus.

This article accounts for the impact of positive and negative shocks of the news-related Climate Policy Uncertainty (CPU) and the novel Economist Intelligence Unit's report-based global Energy Uncertainty (EU) on the U.S. sectoral stock returns by using the ARDL and NARDL approaches with dynamic multiplier simulations. We also utilize both the DCC-GARCH and ADCC-GARCH approaches to extract the symmetric and asymmetric dynamic conditional correlations between the EU and the U.S. sectoral stock returns and then regress these conditional correlation series on the CPU through series of quantile regressions. Overall, the findings suggest that only the positive CPU shocks negatively impact the U.S. sectoral stock returns of Consumer Services, Financials, Industries, Telecommunication and Utilities in the long-term, whereas the negative CPU shocks insignificantly predict the U.S. sectoral returns. The findings also report that only the negative EU shocks increase the U.S. sectoral stock returns of Consumer Services, Financials, Health Care, Industries, Moreover, the positive (negative) EU shocks cause the U.S. sectoral returns of Materials and Technology to decrease (increase) in the long-term. Portfolio managers may consider diversifying their portfolios to include sectors least susceptible to negative impacts from the CPU and EU shocks such as Health Care and Oil & Gas. Our findings also show that CPU shocks moderate the dynamic conditional correlations between the EU and the U.S. sectoral returns of Consumer Services, Materials, Health Care, Telecommunication, Oil and Gas and Utility. Fund managers should contemplate augmenting the allocations to the Financials, Industrials, and Technology sectors owing to their diminished interconnectivity with the EU during periods of heightened CPU.

Green missing spots: Information entropy on greenhouse gas emission disclosure by Brazilian companies.

This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables. Our findings, derived from the data sample of 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy. Additionally, we highlight the pivotal role of public policies in enhancing the availability of GHG emissions data, which, in turn, positively impacts policy-making practices. By demonstrating the potential for a virtuous cycle between improved information availability and enhanced policy outcomes, our research underscores the importance of addressing uncertainty in carbon emissions measurement for advancing effective climate change mitigation strategies.

Driving change: Understanding consumers' reasons influencing electric vehicle adoption from the lens of behavioural reasoning theory.

The present study attempts to explore consumer-centric reasons affecting the adoption of electric vehicles (EVs) are investigated using behavioural reasoning theory (BRT). Our study is among the first to examine consumer's EV adoption intention using BRT through the integration of the reasons "for and against" electric vehicle (EV) adoption. On data of 312 urban consumers, second order confirmatory factor analysis (CFA) revealed the existence of underlying reasons and SEM helped in testing the proposed relationships. This study also investigates the interaction effect of financial incentive policy with the consumer reasons on EV adoption. Findings revealed that "reasons for" adoption are environmental concern, perceived technology, and maintenance of knowledge and "reasons against" adoption are scepticism, price, and instrumental utility. Environmental beliefs and values influence the "reasons for" consumer intentions to approve electric vehicle adoption. Financial incentives policy was found significant in dampening the impact of reasons against adoption of electric vehicle. The study delineates the strategies for strengthening the promotion of electric vehicles.

Sustainable consumption behaviour: Mediating role of pro-environment self-identity, attitude, and moderation role of environmental protection emotion.

This study aims to examine how these factors-egoistic and altruistic values and social consumption motivation-stimulus factors-influence sustainable consumption behaviour (response factor), the mediating role of pro-environmental self-identity and attitude (organism factor), and the moderating effect of environmental protection emotion. We used the stimulus organism response theory to gain a solid and holistic understanding of the concept. We collected 328 original surveys from Indian customers who recently purchased sustainable products via convenience and purposive sampling, and we analysed the data by using structural equation modelling. Intriguingly, the results show that (a) stimulus factors, such as egoistic and altruistic values and social consumption motivation, had a positive influence on pro-environment self-identity and attitude and sustainable consumption behaviour; (b) pro-environment self-identity had a positive effect on attitude; (c) mediation factors (pro-environment self-identity and attitude) had a significantly positive mediation relationship between stimulus and response factors; and (d) environmental protection emotion significantly moderated the relationship between stimulus, organism factors, and response factors. The study contributes to the current body of knowledge in the area of sustainable marketing by examining the rapidly growing phenomenon of sustainable consumer behaviour.

Green bond vs. Islamic bond: Which one is more environmentally friendly?

This research investigates the dynamic dependence and causality relationship of the S&P Kensho Clean Energy (CE) and Cleantech (CT) indices with two green bond indices, including the S&P Green Bond Index (GB) and Green Bond Select (GBS) indices, and four Islamic bond indices, including A-, AA-, AAA-, and BBB-graded Sukuk Indices. In the long- and medium-term, the dependence of CE and CT on Sukuk and green bond indices strengthens under normal or bearish market conditions based on quantile cross-spectral (coherency). Overall, among all alternative financing instruments, AA- and A-rated Sukuk indices and GBS indices have higher coherency with CE and CT. Furthermore, the direction of causality in the frequency domain is most commonly observed from Sukuk and bond indices to CT and CE. Once again, AA-rated, A-rated, and GBS have more significant causality effects on CT and CE for nearly all frequencies compared to other indices. These results present vital implications for both policymakers who want to advocate the development of environmentally friendly projects and investors who want to invest their capital in green economic activities in terms of diversifying and hedging their investments.

Green factors stimulating the purchase intention of innovative luxury organic beauty products: Implications for sustainable development.

Orienting consumers toward organic eco-friendly beauty products is a societal challenge that resonates with growing environmental concerns following COP 21, when entrepreneurs in the cosmetic industry initiated ambitious plans to increase the proportion of natural components in their products. This study examines how green factors impact customers' intention to purchase premium organic beauty products. We explore the mediating roles of trust and attitude on this purchase intention, using the SOR paradigm and the dual factor theory, an original approach. Data was collected from 398 respondents using convenience sampling and analysed using the IBM SPSS 22 version and AMOS 22 version package applying structural equation modelling. We show that the mediating role of trust and attitude is of the utmost importance to ensure a sustainable orientation of customers toward organic products. The findings suggest (a) green ads, green brand image and perceived consumer effectiveness positively influence consumers' attitude toward luxury organic beauty products; (b) LOHAS consumption tendency does not impact consumers' attitudes; (c) trust has a significantly negative impact on the consumer's attitude; and (d) likewise, consumers' attitude mediate the intention to purchase luxury organic beauty products. The research has significant implications for understanding India's premium organic beauty goods market and for companies to develop new strategies to market their organic cosmetics. The study also encourages policymakers to provide necessary subsidies to new organic beauty entrepreneurs.

Customer integration in the supply chain: the role of market orientation and supply chain strategy in the age of digital revolution.

Ever increasing demand for customization and product diversity from the customers has made it important for firms to predict changes in the customer demand patterns and adopt accordingly. Customer integration allows firms to understand customers and respond to their particular needs in a better way. This study investigates the mechanisms through which customer integration is developed and affects supply chain performance. We develop a structural model underlining the role of market orientation and supply chain strategy as factors affecting the degree of customer integration. We also investigate the contingency role of marketing - supply chain integration in these relationships. We test the hypothesized model using data from Pakistani manufacturing organizations using structural equation modelling. Our results provide support for the study hypotheses except that marketing-supply chain alignment does not moderate the relationship between supply chain strategy and customer integration.