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Search Results (535)

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Keywords = economic policy uncertainty

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15 pages, 495 KiB  
Article
How Do Macroeconomic Cycles and Government Policies Influence Cash Holdings? Evidence from Listed Firms in China
by Fangnan Cui, Yue Tan and Bangwen Lu
Sustainability 2024, 16(18), 7961; https://doi.org/10.3390/su16187961 - 12 Sep 2024
Viewed by 237
Abstract
Cash holdings are vital for a firm’s resilience and ability to capitalize on investment opportunities amid economic fluctuations. In this study, the complex relationship between macroeconomic cycles, government policies, and the cash holdings of Chinese listed firms is investigated. By analyzing data from [...] Read more.
Cash holdings are vital for a firm’s resilience and ability to capitalize on investment opportunities amid economic fluctuations. In this study, the complex relationship between macroeconomic cycles, government policies, and the cash holdings of Chinese listed firms is investigated. By analyzing data from Shanghai and Shenzhen A-share listed firms from 2004 to 2019, this research uncovers the individual and combined effects of economic cycles and monetary policies on corporate cash management. Key findings include the following: (1) A significant negative correlation between cash holdings and economic cycle volatility indicates that firms tend to increase cash holdings during periods of instability and reduce them during economic stability. (2) There is a strong negative relationship between restrictive monetary policy and cash holdings, suggesting that firms accumulate more cash to safeguard against tighter financial conditions. (3) The interplay between economic policies and business cycles reveals that during recessions, restrictive monetary policy increases cash holdings, while economic policy uncertainty reduces them. In contrast, during economic prosperity, monetary policy has a minimal impact on cash holdings. These insights emphasize the need for firms to integrate both economic cycles and policy environments into their cash management strategies. The findings offer valuable guidance for policymakers and business leaders aiming to enhance financial stability and optimize cash holdings across different economic conditions. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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<p>Economic policy uncertainty and GDP growth rate trends.</p>
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16 pages, 328 KiB  
Article
Economic Policy Uncertainty, Managerial Ability, and Cost of Equity Capital: Evidence from a Developing Country
by Arafat Hamdy, Aref M. Eissa and Ahmed Diab
Economies 2024, 12(9), 244; https://doi.org/10.3390/economies12090244 - 11 Sep 2024
Viewed by 336
Abstract
This study investigates the relationship between economic policy uncertainty (EPU) and the cost of equity capital (CoEC). It also reveals the moderating role of managerial ability (MA) in the relationship between EPU and CoEC in Saudi Arabia. The study sample consists of listed [...] Read more.
This study investigates the relationship between economic policy uncertainty (EPU) and the cost of equity capital (CoEC). It also reveals the moderating role of managerial ability (MA) in the relationship between EPU and CoEC in Saudi Arabia. The study sample consists of listed non-financial firms in Tadawul from 2008 to 2019. We analyzed data using STATA, depending on Pearson correlation analysis, two independent sample t-tests, OLS regression, and OLS with robust standard errors clustered by firm. Our study shows a positive effect of EPU on the CoEC. In addition, the results confirm that MA mitigates the positive effect of EPU on the CoEC. This is the first research to investigate the influence of the relationship between EPU on CoEC in Saudi Arabia, one of the largest emerging economies in the Middle East and Gulf countries. Our findings motivate decision-makers to strengthen their MA and establish a safe and stable investment environment to ensure better financing and investment decisions during uncertain times. Lending agencies, investors, and other stakeholders should consider the MA of corporations when making investment decisions. Full article
(This article belongs to the Special Issue Financial Market Volatility under Uncertainty)
17 pages, 1068 KiB  
Article
Brace for Impact: Facing the AI Revolution and Geopolitical Shifts in a Future Societal Scenario for 2025–2040
by Michael Gerlich
Societies 2024, 14(9), 180; https://doi.org/10.3390/soc14090180 - 11 Sep 2024
Viewed by 599
Abstract
This study investigates the profound and multifaceted impacts of Artificial Intelligence (AI) and geopolitical developments on global dynamics by 2040. Utilising a Delphi process coupled with probabilistic modelling, the research constructs detailed scenarios that reveal the cascading effects of these emerging forces across [...] Read more.
This study investigates the profound and multifaceted impacts of Artificial Intelligence (AI) and geopolitical developments on global dynamics by 2040. Utilising a Delphi process coupled with probabilistic modelling, the research constructs detailed scenarios that reveal the cascading effects of these emerging forces across economic, societal, and security domains. The findings underscore the transformative potential of AI, predicting significant shifts in employment patterns, regulatory challenges, and societal structures. Specifically, the study forecasts a high probability of AI-induced unemployment reaching 40–50%, alongside the rapid evolution of AI technologies, outpacing existing governance frameworks, which could exacerbate economic inequalities and societal fragmentation. Simultaneously, the study examines the critical role of geopolitical developments, identifying increased nationalisation, the expansion of conflicts such as the Russia–Ukraine war, and the strategic manoeuvres of major powers like China and Israel as key factors that will shape the future global landscape. The research highlights a worrying lack of preparedness among governments and societies, with a 10% probability of their being equipped to manage the complex risks posed by these developments. This low level of readiness is further complicated by the short-term orientation prevalent in Western businesses, which prioritise immediate returns over long-term strategic planning, thereby undermining the capacity to respond effectively to these global challenges. The study calls for urgent, forward-looking policies and international cooperation to address the risks and opportunities associated with AI and geopolitical shifts. It emphasises the need for proactive governance, cross-sector collaboration, and robust regulatory frameworks to ensure that the benefits of technological and geopolitical advancements are harnessed without compromising global stability or societal well-being. As the world stands on the brink of unprecedented change, the findings of this study provide a crucial roadmap for navigating the uncertainties of the future. Full article
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<p>CAS framework [<a href="#B21-societies-14-00180" class="html-bibr">21</a>].</p>
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<p>Simplified graphic display of the impact chain for AI.</p>
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<p>Simplified graphic display of the impact chain for geo-political developments.</p>
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18 pages, 840 KiB  
Article
Sustainable Agritourism for Farm Profitability: Comprehensive Evaluation of Visitors’ Intrinsic Motivation, Environmental Behavior, and Satisfaction
by Jibin Baby and Dae-Young Kim
Land 2024, 13(9), 1466; https://doi.org/10.3390/land13091466 - 10 Sep 2024
Viewed by 322
Abstract
Unstable farm income and the desire to diversify revenue sources have increased the significance of agritourism as an alternative economic opportunity for farmers and ranchers. Agritourism integrates the top economic drivers—agriculture and tourism—and has been identified as a highly effective complementary business for [...] Read more.
Unstable farm income and the desire to diversify revenue sources have increased the significance of agritourism as an alternative economic opportunity for farmers and ranchers. Agritourism integrates the top economic drivers—agriculture and tourism—and has been identified as a highly effective complementary business for farmers to generate additional income and mitigate the financial uncertainties associated with traditional farming enterprises. Visitors’ satisfaction is critical for operating a successful agritourism business, as it influences destination choice, consumption of products and services, and the decision to return. This study examined the relationship between agritourism visitors’ intrinsic motivation, environmental behavior, satisfaction, and intentions to revisit and recommend. With a total of 615 survey responses, the study reveals a significant relationship between agritourism visitors’ intrinsic motivation, environmental behavior, and satisfaction related to destination, risk, and food attributes. Furthermore, visitors’ overall satisfaction with these three attributes significantly influences their intentions to revisit and recommend the destination. The findings of this study will enable agritourism operators and policymakers to formulate appropriate policies for the sustainable development of this sector. Future promotional and educational tools could be developed based on these findings. Full article
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<p>Research framework.</p>
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<p>Result of hypotheses analysis—structural equation modeling.</p>
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26 pages, 10477 KiB  
Article
Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization
by Dongbao Jia, Ming Cao, Jing Sun, Feimeng Wang, Wei Xu and Yichen Wang
Electronics 2024, 13(17), 3579; https://doi.org/10.3390/electronics13173579 - 9 Sep 2024
Viewed by 367
Abstract
Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable [...] Read more.
Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty. Full article
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<p>IIES architecture.</p>
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<p>MOMAML-PPO solution process.</p>
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<p>Solution selection at interval knee points.</p>
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<p>Meta-learning training.</p>
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<p>Schematic of the enhanced PPO-CLIP method.</p>
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<p>Forecasting renewable energy output and multiple load demands.</p>
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<p>Comparison of wind power output intervals under different confidence levels.</p>
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<p>The 95% confidence interval forecasting for renewable energy output and load demands.</p>
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<p>Pareto frontier of the ICMOP solution. (<b>a</b>) Pareto front boundary point plot; (<b>b</b>) Pareto front matrix plot.</p>
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<p>Scheduling results.</p>
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<p>Sensitivity analysis of the learning rate parameter in actor–critic networks.</p>
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<p>Sensitivity analysis of the reward discount factor in coefficient parameters.</p>
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<p>Average reward change curve.</p>
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<p>Dispatch strategy under emergency conditions: (<b>a</b>) scheduling results for Emergent Scenario 1; (<b>b</b>) scheduling results for Emergent Scenario 2.</p>
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52 pages, 6746 KiB  
Article
COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL
by Emna Trabelsi
J. Risk Financial Manag. 2024, 17(9), 403; https://doi.org/10.3390/jrfm17090403 - 9 Sep 2024
Viewed by 374
Abstract
This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are [...] Read more.
This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended. Full article
(This article belongs to the Special Issue Stability of Financial Markets and Sustainability Post-COVID-19)
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<p>Evolution of TUNINDEX stock return (2 January 2020–30 December 2022). Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas and indicating the autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus the COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas, indicating autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Tunisian realized volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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<p>Wavelet transform coherence: Tunisian realized volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.</p>
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43 pages, 2810 KiB  
Article
Corporate Financial Performance vs. Corporate Sustainability Performance, between Earnings Management and Process Improvement
by Valentin Burcă, Oana Bogdan, Ovidiu-Constantin Bunget and Alin-Constantin Dumitrescu
Sustainability 2024, 16(17), 7744; https://doi.org/10.3390/su16177744 - 5 Sep 2024
Viewed by 515
Abstract
The main objective of the paper is to assess the relationship between firms’ financial resilience and firms’ strategic sustainable development vulnerabilities, in the context of implications of the COVID-19 pandemic on firms’ business environment. Background: The last decade has emphasized an increase in [...] Read more.
The main objective of the paper is to assess the relationship between firms’ financial resilience and firms’ strategic sustainable development vulnerabilities, in the context of implications of the COVID-19 pandemic on firms’ business environment. Background: The last decade has emphasized an increase in business models’ uncertainty and risk exposure. The COVID-19 pandemic has highlighted the awareness in this direction, especially in a changing context, that looks more and more for corporate sector operations’ orientation towards sustainable development. The question we would address in this paper is how the nexus between corporate sustainability performance and corporate financial resilience is affected by management decision through process improvements, product quality assurance, or managers’ preference to improve corporate financials by earnings management practice instead, especially in the context of specific corporate financial risk management. Methods: The data are extracted from the Refinitiv database. The sample is limited to 275 European Union listed firms, selected based on data availability. The empirical analysis consists of an OLS multiple regression. For robustness purposes, a quantile regression model is estimated as well. Results: The approach considers implications of the pandemic on firms’ business environment and earnings management accounting based policies and strategies as well. The result suggests that alignment to sustainability frameworks lead to the deterioration of firms’ financial resilience. Similar results show the negative impact of firms’ financial vulnerability (credit default risk) on firms’ financial resilience. Instead, the risk of bankruptcy, firms’ liquidity, or high product quality and business process improvement determine the positive impact on firms’ financial resilience. Conclusions: The study highlights several insights both for management and policy makers. First, the results underline the relevance of management’s choice for earnings management on ensuring firms’ financial resilience, which ask for better corporate governance and high-quality and effective institutional regulatory and enforcement mechanisms. Second, the paper brings evidence on the impact of the COVID-19 pandemic on firms’ financial sustainable development. Third, the study emphasizes the importance of the efforts of corporate process improvements and high-quality products on generating value-add, by looking on the relevance of those drivers on the level of corporate economic value-add, a measure that limits the impact of discretionary management accrual-based accounting choices on our discussion. Full article
(This article belongs to the Special Issue Management Control Systems to Sustainability)
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<p>Theoretical research framework. Source: authors’ projection.</p>
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<p>Empirical analysis methodological steps. Source: authors’ projection.</p>
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<p>Sample composition. Source: authors’ projection.</p>
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<p>Time series on the evolution of EVA on country basis. Source: authors’ projection.</p>
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<p>Time series on the evolution of EVA on industry basis. Source: authors’ projection.</p>
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<p>Industry effects on variation of economic value added in pandemic period. Source: authors’ projection.</p>
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22 pages, 1242 KiB  
Article
Forest Products Trade and Sustainable Development in China and the USA: Do Bioenergy and Economic Policy Uncertainty Matter?
by Li Mi, Yongjun Huang, Muhammad Tayyab Sohail and Sana Ullah
Forests 2024, 15(9), 1505; https://doi.org/10.3390/f15091505 - 28 Aug 2024
Viewed by 493
Abstract
The United Nations Agenda 2030 for Sustainable Development has induced the empirics to find the factors that can contribute to sustainable development. However, limited empirical evidence has estimated the impact of forest trade, bioenergy, and economic policy uncertainty on sustainable development. This study [...] Read more.
The United Nations Agenda 2030 for Sustainable Development has induced the empirics to find the factors that can contribute to sustainable development. However, limited empirical evidence has estimated the impact of forest trade, bioenergy, and economic policy uncertainty on sustainable development. This study fills the gap by analyzing the impact of forest trade, bioenergy, and economic policy uncertainty on sustainable development in China and the USA using the ARDL and QARDL approaches. The findings of the ARDL model suggest that forest trade helps boost both short- and long-run sustainable development in China and the USA, while bioenergy fosters sustainable development in the short and long run only in China and in the USA, bioenergy improves sustainable development only in the long run. In contrast, economic policy uncertainty hurts sustainable development in the short and long run in China, while in the USA, only the long-run negative association between the two variables is observed. Thus, policymakers in China and the USA need to focus on enhancing trade in forest products, fostering bioenergy generation, and reducing uncertainties in economic policy to promote sustainable development. Full article
(This article belongs to the Special Issue Economy and Sustainability of Forest Natural Resources)
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<p>Econometric methodology flowchart.</p>
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<p>China’s trends in forest trade, bioenergy, and economic policy uncertainty.</p>
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<p>USA trends of forest trade, bioenergy, and economic policy uncertainty.</p>
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18 pages, 3569 KiB  
Article
Research on Energy Management in Hydrogen–Electric Coupled Microgrids Based on Deep Reinforcement Learning
by Tao Shi, Hangyu Zhou, Tianyu Shi and Minghui Zhang
Electronics 2024, 13(17), 3389; https://doi.org/10.3390/electronics13173389 - 26 Aug 2024
Viewed by 549
Abstract
Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion, utilization, and storage technologies with distributed wind and photovoltaic power generation techniques, it is possible to achieve complementary utilization and synergistic operation of multiple energy sources in the form [...] Read more.
Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion, utilization, and storage technologies with distributed wind and photovoltaic power generation techniques, it is possible to achieve complementary utilization and synergistic operation of multiple energy sources in the form of microgrids. However, the diverse operational mechanisms, varying capacities, and distinct forms of distributed energy sources within hydrogen-coupled microgrids complicate their operational conditions, making fine-tuned scheduling management and economic operation challenging. In response, this paper proposes an energy management method for hydrogen-coupled microgrids based on the deep deterministic policy gradient (DDPG). This method leverages predictive information on photovoltaic power generation, load power, and other factors to simulate energy management strategies for hydrogen-coupled microgrids using deep neural networks and obtains the optimal strategy through reinforcement learning, ultimately achieving optimized operation of hydrogen-coupled microgrids under complex conditions and uncertainties. The paper includes analysis using typical case studies and compares the optimization effects of the deep deterministic policy gradient and deep Q networks, validating the effectiveness and robustness of the proposed method. Full article
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<p>A typical structure of a hydrogen–electric coupling microgrid.</p>
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<p>Structure of the DDPG algorithm.</p>
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<p>Typical curves of solar photovoltaic output, charging load forecast, and hydrogen charging load forecast.</p>
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<p>Pricing signal.</p>
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<p>Energy management strategy reward convergence curve.</p>
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<p>Hydrogen tank and electrical energy storage system operational strategies.</p>
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<p>The charging strategy for microgrids.</p>
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<p>Hydrogen loading strategies for microgrids under different algorithms.</p>
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<p>The purchasing strategy of microgrids from the public electricity grid.</p>
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18 pages, 1556 KiB  
Article
The Key Factors for Improving Returns Management in E-Commerce in Indonesia from Customers’ Perspectives—An Analytic Hierarchy Process Approach
by Dimas Haki Prayogo, Roman Domanski and Paulina Golinska-Dawson
Sustainability 2024, 16(17), 7303; https://doi.org/10.3390/su16177303 - 25 Aug 2024
Viewed by 504
Abstract
The rapid growth of e-commerce has led to an increase in the number of product returns in supply chains, which is both environmentally and economically challenging. E-commerce companies need to effectively manage product returns, as this has a direct impact on their reputation [...] Read more.
The rapid growth of e-commerce has led to an increase in the number of product returns in supply chains, which is both environmentally and economically challenging. E-commerce companies need to effectively manage product returns, as this has a direct impact on their reputation and consumer experience. Reducing returns is key to maintaining sustainable practices for online product sales. A significant increase in e-commerce transactions is also evident in Indonesia, which is the fourth largest country in the world. Despite the very large size of the market, research on e-commerce in the business-to-customer (B2C) market in Indonesia is underrepresented in the literature. The purpose of this paper is to identify key factors from the customer perspective that influence product returns in reverse logistics in Indonesian e-commerce. The novelty of this study stems from the focus on the customer perspective on product returns in the B2C market when shopping online and the spatial scope. Due to the uncertainty inherent in multi-criteria decision making, the analytic hierarchy process (AHP) method was used to rank factors and potential solutions derived from a critical literature review. As a result, the study provides a ranking of factors and alternatives for managing e-commerce returns in Indonesia. The results show that among Indonesian e-commerce customers, product quality (QP) was rated the highest, while (Pu) was rated the lowest. In terms of the alternatives that are the most suitable for improving the customer experience of e-commerce product returns in Indonesia, a clear returns policy (CRP) scored the highest, while the merchandise catalog (Cat) was rated as the lowest priority. Full article
(This article belongs to the Special Issue Recent Advances in Modern Technologies for Sustainable Manufacturing)
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<p>Concept of reverse logistics, adopted from [<a href="#B47-sustainability-16-07303" class="html-bibr">47</a>].</p>
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<p>Problem structure for AHP.</p>
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<p>Research methodology.</p>
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38 pages, 773 KiB  
Article
Book-Tax Differences during the Crisis: Does Corporate Social Responsibility Matter?
by Prianto Budi Saptono, Gustofan Mahmud, Intan Pratiwi, Dwi Purwanto, Ismail Khozen, Lambang Wiji Imantoro and Maria Eurelia Wayan
Sustainability 2024, 16(17), 7271; https://doi.org/10.3390/su16177271 - 23 Aug 2024
Viewed by 683
Abstract
This study investigates the intricate relationship between corporate financial strategies, encapsulated by book-tax differences (BTDs), and firms’ engagement in corporate social responsibility (CSR) programs during economic crises. Using an unbalanced panel dataset drawn from financial, annual, and sustainability reports of over 97 Indonesian [...] Read more.
This study investigates the intricate relationship between corporate financial strategies, encapsulated by book-tax differences (BTDs), and firms’ engagement in corporate social responsibility (CSR) programs during economic crises. Using an unbalanced panel dataset drawn from financial, annual, and sustainability reports of over 97 Indonesian non-financial firms from 2017 to 2022, this study reveals that economic crises and CSR activities positively influence total BTD and permanent differences. Notably, firms strategically leverage CSR initiatives amidst crises to enhance their corporate image and manage internal challenges like aggressive tax planning. The robustness of these findings was validated through endogeneity analysis and by examining sub-samples from industries most impacted by the pandemic. In the industries least affected by the pandemic, the direct impact of CSR on BTD was found to be negative, indicating that in the general context, the CSR programs held by these industries are largely driven by normative motives. However, when specified in the crisis context, CSR serves as a strategic buffer for these industries, which reaffirms the prevalence of CSR strategic motives during Indonesia’s pandemic challenges. The findings suggest policy implications for shareholders, regulators, and policymakers to ensure CSR transparency aligns with long-term corporate values and societal impact, incentivizing genuine CSR practices amidst economic uncertainty. Despite its contributions, the study recommends future research explore different domains of CSR and validate findings across diverse contexts to enrich the understanding of CSR’s role in corporate resilience strategies. Full article
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<p>Average BTD of Companies in Indonesia Before and During the Crisis Period.</p>
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<p>Average CSR Activities of Companies in Indonesia Before and During the Crisis Period.</p>
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<p>Scatter Plot of CSR Activities and BTD.</p>
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15 pages, 2108 KiB  
Article
Social Understanding of Disability: Determinants and Levers for Action
by Ulysse Lecomte, Araceli de los Ríos Berjillos, Laetitia Lethielleux, Xavier Deroy and Maryline Thenot
Behav. Sci. 2024, 14(9), 733; https://doi.org/10.3390/bs14090733 - 23 Aug 2024
Viewed by 491
Abstract
The prejudices often associated with the perception of people with disability can limit their access to the opportunities and resources available in society, leading them to live in a climate of great socio-economic uncertainty exacerbated since the COVID-19 pandemic. This research focuses on [...] Read more.
The prejudices often associated with the perception of people with disability can limit their access to the opportunities and resources available in society, leading them to live in a climate of great socio-economic uncertainty exacerbated since the COVID-19 pandemic. This research focuses on the perceptions of young people in France, defined as those aged between 18 and 30, towards people with disability. The study draws on the principles of social psychology to understand these perceptions, the factors that influence them and the most effective ways of promoting greater inclusion. A survey of 660 young people confirms that, despite recent progress, people with disabilities are still perceived as socially excluded. The results show that familiarity with disability, open-mindedness, the visibility of disability and the quality of interactions with people with disabilities have a strong influence on perceptions. To improve these perceptions, disability training and awareness raising are considered more effective than communication or positive discrimination measures. This research is the first to explore perceptions of disability among young people in France, with the potential to influence future behavior. It suggests ways to promote effective inclusive practices and support policies that encourage positive interactions with people with disabilities. Full article
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<p>Snapshot of the new generation’s perception of disability.</p>
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<p>Changes in discrimination against people with disabilities based on responses from the New Generation sample.</p>
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<p>Changes in the inclusion of people with disabilities in the main living spaces of everyday life according to the New Generation sample.</p>
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<p>Current levels of social inclusion of people with disabilities according to our New Generation sample.</p>
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23 pages, 696 KiB  
Article
The Effect of Trade Policy Uncertainty on Green Technology Innovation: Evidence from China’s Enterprises
by Xiaoling Zhao, Mingru Peng, Xing Wang, Rui Xu and Yongze Cui
Sustainability 2024, 16(16), 7150; https://doi.org/10.3390/su16167150 - 20 Aug 2024
Viewed by 748
Abstract
Promoting green technology innovation is essential for sustainable development and the transition to a low-carbon economy. Using data from listed manufacturing companies in China from 2000 to 2020, this paper takes the establishment of permanent normal trade relations with the United States after [...] Read more.
Promoting green technology innovation is essential for sustainable development and the transition to a low-carbon economy. Using data from listed manufacturing companies in China from 2000 to 2020, this paper takes the establishment of permanent normal trade relations with the United States after China’s accession to the WTO as a quasi-natural experiment and uses the difference-in-differences method to study the impact of the decline in trade policy uncertainty on firms’ green technology innovation. The results show the following: (1) Reduced trade policy uncertainty significantly enhances green technology innovation in firms. (2) Further research finds that the decline in trade policy uncertainty mainly promotes the level of the green technology innovation of firms by alleviating financing constraints faced by firms and intensifying market competition. (3) A heterogeneity analysis reveals that the impact is more pronounced in firms with lower capital intensity, higher growth, export firms, and firms exporting to the United States. This study offers micro-level empirical evidence from China on the economic outcomes of external trade policy changes from the perspective of firms’ green technology innovation and provides insights into how the government should respond to the risks of external trade frictions and improve firms’ sustainable development in the future. Full article
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<p>Coefficients and 95% confidence intervals for the interaction term <math display="inline"><semantics> <mrow> <mi mathvariant="italic">TPU</mi> <mo>⋅</mo> <mi mathvariant="italic">dumyear</mi> </mrow> </semantics></math>. The solid line represents the dynamic effect over time of reduced trade policy uncertainty on the number of firms’ green patent applications, and the dashed lines indicate the 95% confidence interval.</p>
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27 pages, 4329 KiB  
Article
Digital Platforms as a Fertile Ground for the Economic Sustainability of Startups: Assaying Scenarios, Actions, Plans, and Players
by Morteza Hadizadeh, Javad Ghaffari Feyzabadi, Zahra Fardi, Seyed Morteza Mortazavi, Vitor Braga and Aidin Salamzadeh
Sustainability 2024, 16(16), 7139; https://doi.org/10.3390/su16167139 - 20 Aug 2024
Viewed by 691
Abstract
This study examines the transformative role of digital platforms in fostering sustainable entrepreneurship within emerging economies. We argue that platforms transcend mere communication channels, acting as catalysts for innovation and collaboration among startups, thereby driving economic, social, and environmental progress. Our framework emphasizes [...] Read more.
This study examines the transformative role of digital platforms in fostering sustainable entrepreneurship within emerging economies. We argue that platforms transcend mere communication channels, acting as catalysts for innovation and collaboration among startups, thereby driving economic, social, and environmental progress. Our framework emphasizes platform-enabled startups, navigating the unique challenges and opportunities presented by these dynamic markets. We adopt a dual lens, using a mixed-methods approach to analyze digital development trends through the prism of platforms in emerging economies. This reframes the discourse on technology-driven development, acknowledging the unidirectional flow of platform adoption from developed nations. The research emphasizes the need for prioritizing sustainability standards in these regions. Furthermore, we delve into the interplay between platforms and sustainable entrepreneurship with the following three objectives: (1) deciphering the drivers of platform–startup interaction for sustainability goals, (2) formulating policies to maximize platform benefits while mitigating risks, and (3) developing actionable strategies for stakeholders to cultivate a thriving ecosystem of sustainable platform-based ventures. The findings of this study reveal six key uncertainties that will shape the future trajectories of sustainable entrepreneurship within digital ecosystems, particularly in developing nations. These uncertainties encompass the following: environmental and social standards, ongoing education and development, mobile application development and utilization, global market access, and competitiveness and value creation. Several alternative future scenarios have been constructed based on these uncertainties, including advancements in digital technologies, dynamic market conditions and evolving consumer behaviors, a heightened emphasis on sustainability and corporate social responsibility, and a paradigm shift towards collaborative business models. A comprehensive framework of supportive policies and interventions has been proposed to facilitate the realization of these scenarios. Moreover, the analysis underscores the pivotal roles of digital platform providers and startups as key stakeholders in this evolving landscape. Full article
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<p>Influence–dependence map of drivers.</p>
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<p>Actions–policies profile map.</p>
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<p>Classification sensitivity map.</p>
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<p>Action–policy closeness map.</p>
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<p>Policies–scenarios profile map.</p>
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<p>Policies’ classification sensitivity map.</p>
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<p>Policy–scenario closeness map.</p>
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<p>Potential paths from actions and policies to reach alternative scenarios.</p>
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<p>Strategic Influence and Dependency Map for Digital Platform Ecosystem Actors.</p>
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<p>The Histogram of Competitiveness.</p>
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<p>Interactor distance and cohesion map in the digital ecosystem.</p>
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<p>Alignment and discrepancy map of strategic objectives.</p>
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20 pages, 6701 KiB  
Article
ESG-Driven Investment Decisions in Photovoltaic Projects
by Ruolan Wei, Yunlong Ma, Huina Bi and Qi Dong
Energies 2024, 17(16), 4117; https://doi.org/10.3390/en17164117 - 19 Aug 2024
Viewed by 362
Abstract
As global climate change intensifies and environmental awareness increases, investing in renewable energy has become a primary economic and social development priority. Photovoltaic (PV) projects, as a clean and sustainable energy technology, have garnered significant attention due to their notable environmental and economic [...] Read more.
As global climate change intensifies and environmental awareness increases, investing in renewable energy has become a primary economic and social development priority. Photovoltaic (PV) projects, as a clean and sustainable energy technology, have garnered significant attention due to their notable environmental and economic benefits. However, traditional investment evaluation methods such as net present value (NPV) analysis fail to adequately capture the flexibility and future uncertainties inherent in PV project investments. This paper presents a case study analysis proposing a delay option model that incorporates environmental, social, and governance (ESG) factors, providing a more scientific and flexible investment decision framework for PV projects. The case study results indicate that considering ESG factors significantly enhances the investment value of PV projects. This model not only provides comprehensive support for PV project investment decisions but also underscores the importance of establishing stringent carbon trading markets and policy incentive mechanisms to promote the widespread adoption and sustainable development of renewable energy projects. Full article
(This article belongs to the Section C: Energy Economics and Policy)
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<p>Research logic framework of the investment decision model for photovoltaic projects.</p>
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<p>Monthly electricity price changes over the 25-Year operating period of the PV project.</p>
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<p>Monthly operating cost changes over the 25-year operating period of the PV project.</p>
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<p>Carbon trading price forecast by the Fudan University Research Center for Sustainable Development.</p>
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<p>Sample paths of electricity price changes simulated using the O-U process(different colors represent different paths).</p>
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<p>Sample paths of operating cost changes simulated using GBM(different colors represent different paths).</p>
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<p>NPV (results of 1000 simulation experiments).</p>
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<p>Investment value (results of 1000 simulation experiments).</p>
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<p>Delayed option value (results of 1000 simulation experiments).</p>
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<p>Optimal investment timing.</p>
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<p>Investment value of the PV project (without considering ESG factors).</p>
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<p>Impact of considering and not considering ESG factors on the investment value of the PV project.</p>
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<p>Sensitivity analysis of project NPV.</p>
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<p>Sensitivity analysis of project investment value.</p>
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<p>Sensitivity analysis of project delayed option value.</p>
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