Search Results (100)

This research aims to investigate financing decisions of capital-constrained small and medium-sized enterprise (SME) manufacturers and distributors under a Green Supply Chain (GSC) framework. By evaluating the impact of Supply Chain Finance (SCF) instruments, this study utilizes Stackelberg game model to explore a decentralized decision-making system. To our knowledge, this investigation represents the first exploration of game models that uniquely compares financing through trade credit, where the manufacturer offers zero-interest credit without discounts with reverse factoring, while also considering distributor’s efforts on sustainable marketing under the impact of supportive government policies. Our study suggests that manufacturers should adopt reverse factoring for optimal profits and actively participate in distributors’ financing decisions to address inefficiencies in decentralized systems. Furthermore, the distributor’s demand quantity, profits and sustainable marketing efforts show significant increase under reverse factoring, aided by favorable policies. Finally, the results are validated through Python 3.8.8 simulations in the Anaconda distribution, offering meaningful insights for policymakers and supply chain managers. Full article

This article investigates the macroeconomic and labor market conditions that shape the adoption of artificial intelligence (AI) technologies among large firms in Europe. Based on panel data econometrics and supervised machine learning techniques, we estimate how public health spending, access to credit, export activity, gross capital formation, inflation, openness to trade, and labor market structure influence the share of firms that adopt at least one AI technology. The research covers all 28 EU members between 2018 and 2023. We employ a set of robustness checks using a combination of fixed-effects, random-effects, and dynamic panel data specifications supported by Clustering and supervised learning techniques. We find that AI adoption is linked to higher GDP per capita, healthcare spending, inflation, and openness to trade but lower levels of credit, exports, and capital formation. Labor markets with higher proportions of salaried work, service occupations, and self-employment are linked to AI diffusion, while unemployment and vulnerable work are detractors. Cluster analysis identifies groups of EU members with similar adoption patterns that are usually underpinned by stronger economic and institutional fundamentals. The results collectively suggest that AI diffusion is shaped not only by technological preparedness and capabilities to invest but by inclusive macroeconomic conditions and equitable labor institutions. Targeted policy measures can accelerate the equitable adoption of AI technologies within the European industrial economy. Full article

Carbon cap-and-trade is a practical policy in guiding manufacturers to produce economic and environmental production plans. However, previous studies on carbon cap-and-trade are from a macro level to guide manufacturers to make production plans, rather than from a perspective of specific production scheduling, which leads to a lack of theoretical guidance for manufacturers to develop reasonable production scheduling schemes for specific production orders. This article investigates a specific scheduling problem in a flexible job-shop environment that considers the carbon cap-and-trade policy, aiming to provide guidance for specific production scheduling (i.e., resource allocation). In the proposed problem, carbon emissions have an upper limit. A penalty will be generated if the emissions overpass the predetermined cap. To satisfy the carbon emission cap, the manufacturer can trade carbon credits or adopt outsourcing strategy, that is, outsourcing partial orders to partners at the expense of outsourcing costs. To solve the proposed model, a novel and efficient memetic algorithm (NEMA) is proposed. An initialization method and four local search operators are developed to enhance the search ability. Numerous experiments are conducted and the results validate that NEMA is a superior algorithm in both solution quality and efficiency. Full article

To promote the deepening of reform and the effective implementation of policies, the State Council launched the special supervision of the liquidation of local governments’ arrears in project funds in 2016, which supports the optimization of the government debt structure. Based on the quasi-natural experiment of the special supervision action, in this study, we use the difference-in-difference (DID) method to investigate the effect and mechanism of the optimization of the government debt structure on the financing constraints of private enterprises. This research is particularly relevant for private enterprises, which face acute financing challenges and are critical for promoting inclusive economic growth, employment, and innovation—key pillars of sustainable development. The results are as follows. Firstly, the special supervision significantly reduces the financing constraints of private enterprises. Secondly, it has heterogeneous effects on the financing constraints of different types of enterprises, and the alleviating effect is particularly significant for enterprises that rely on the funding support of local governments. This highlights the importance of institutional reforms in fostering equitable access to financial resources for vulnerable enterprise groups such as private enterprises. Thirdly, the optimization of the government debt structure eases enterprises’ financing constraints by improving their capital turnover and trade credit. By enhancing liquidity and creditworthiness, these changes create a more resilient financial environment for private enterprises, supporting their long-term development and contribution to sustainable economic systems. Full article

Against the backdrop of increasingly severe global climate change, the automotive industry, as a carbon-intensive sector, has found its low-carbon transformation crucial for achieving the “double carbon” goals. This paper constructs manufacturer decision-making models under an oligopolistic market scenario for the single dual-credit policy and the “dual-credit + carbon cap-and-trade” policy, revealing the nonlinear impacts of new energy vehicle (NEV) credit trading prices, carbon trading prices, and credit ratio requirements on manufacturers’ pricing, emission reduction effort levels, and profits. The results indicate the following: (1) Under the “carbon cap-and-trade + dual-credit” policy, manufacturers can balance emission reduction costs and NEV production via the carbon trading market to maximize profits, with lower emission reduction effort levels than under the single dual-credit policy. (2) A rise in credit trading prices prompts hybrid manufacturers (producing both fuel vehicles and NEVs) to increase NEV production and reduce fuel vehicle output; higher NEV credit ratio requirements raise fuel vehicle production costs and prices, suppressing consumer demand. (3) An increase in carbon trading prices raises production costs for both fuel vehicles and NEVs, leading to decreased market demand; hybrid manufacturers reduce emission reduction efforts, while others transfer costs through price hikes to boost profits. (4) Hybrid manufacturers face high carbon emission costs due to excessive actual fuel consumption, driving them to enhance emission reduction efforts and promote low-carbon technological innovation. Full article

This study presents an optimization framework for a utility-scale hybrid power plant (HPP) that integrates wind power plants (WPPs), solar power plants (SPPs), and battery energy storage systems (BESS) using historical and probabilistic weather modeling, regulatory incentives, and multi-objective trade-offs. By employing multi-objective particle swarm optimization (MOPSO), the study simultaneously optimizes three key objectives: economic performance (maximizing net present value, NPV), system reliability (minimizing loss of power supply probability, LPSP), and operational efficiency (reducing curtailment). The optimized HPP (283 MW wind, 20 MW solar, and 500 MWh BESS) yields an NPV of $165.2 million, a levelized cost of energy (LCOE) of $0.065/kWh, an internal rate of return (IRR) of 10.24%, and a 9.24-year payback, demonstrating financial viability. Operational efficiency is maintained with <4% curtailment and 8.26% LPSP. Key findings show that grid imports improve reliability (LPSP drops to 1.89%) but reduce economic returns; higher wind speeds (11.6 m/s) allow 27% smaller designs with 54.6% capacity factors; and tax credits (30%) are crucial for viability at low PPA rates (≤$0.07/kWh). Validation via Multi-Objective Genetic Algorithm (MOGA) confirms robustness. The study improves hybrid power plant design by combining weather predictions, policy changes, and optimizing three goals, providing a flexible renewable energy option for reducing carbon emissions. Full article

In this study, the relationship between economic growth and carbon emissions for the period 1968–2022 in Turkey was evaluated within the framework of the EKC (Environmental Kuznets Curve) hypothesis. In addition, the impacts of renewable energy consumption and agricultural value added on carbon emissions were analyzed using the ARDL bounds testing approach. The validity of the results was also tested using the FMOLS and DOLS methods. The findings confirmed the existence of a cointegration relationship between carbon emissions and per capita income, renewable energy consumption, and agricultural value added. Long-term analyses indicate that renewable energy consumption reduces carbon emissions, whereas growth in agricultural value added leads to an increase in emissions. In addition, it has been determined that the EKC hypothesis is valid in both the long and short terms and that increases in per capita income raise emissions up to a certain threshold and have a mitigating effect when this threshold is exceeded. The results of the short-term analysis showed that the effects of renewable energy consumption vary across periods, and that agricultural value added increases emissions in the short term. This study provides empirical evidence for Turkey by incorporating sectoral variables within the EKC framework and offers meaningful insights for policymakers regarding the environmental impacts of agricultural value added and renewable energy use in the context of a developing country. Accordingly, fiscal policy instruments such as green taxation, carbon credit trading mechanisms, and financial and agricultural subsidies should be more effectively utilized in Turkey to support structural transformation in agriculture and promote the use of clean energy, in line with the findings that suggest the need for targeted agricultural and energy policies aligned with Turkey’s SDG commitments. Full article

Amid intensified global climate mitigation efforts, integrating rail freight into carbon emissions trading schemes became critical under China’s “Dual-Carbon” strategy. Despite rail’s significantly lower emissions intensity compared to road transport, existing pricing frameworks inadequately internalized its environmental externalities, which limited its competitive advantage. To address this gap, this study systematically reviewed international and domestic practices of integrating transport into carbon trading systems and developed a novel “four-layer, three-dimensional” emissions trading scheme (ETS) framework tailored specifically for China’s rail freight sector. Employing a Stackelberg bilevel optimization model, this study analyzed how carbon quotas and pricing influenced rail operators’ pricing and investment decisions. The results showed that under optimized quotas and carbon prices, railway enterprises were able to generate surplus carbon credits, creating new revenue streams and enabling freight rate reductions. This “carbon revenue–freight rate feedback loop” not only delivered environmental benefits but also enhanced rail’s economic competitiveness. Overall, this study significantly advances the understanding of carbon-based pricing mechanisms in railway freight, providing robust theoretical insights and actionable policy guidance for achieving sustainable decarbonization in China’s transport sector. Full article

In the global pursuit of sustainable development and climate change mitigation, reconciling export growth with environmental protection has emerged as a universal challenge. As the world’s largest developing economy, China has traditionally relied on a resource-intensive development model to fuel rapid foreign trade growth. However, this extensive growth pattern has not only led to environmental pollution domestically but has also encountered hurdles from international green trade barriers. Finance, as a key driver of stable economic growth, plays a pivotal role in achieving high-quality trade development. Against this backdrop, the Chinese government has introduced the green credit interest subsidies policy. This policy aims to coordinate government financial resources and guide capital toward green production, alleviating financing constraints and fostering the upgrading of export product quality. Utilizing data from the World Bank, China Customs statistics, and provincial panels from 2011 to 2020, this study employs a multi-period difference-in-differences (DID) model to examine the causal impact of the green credit subsidies policy on efforts to upgrade the export quality of green products across China’s regions. The benchmark regression results indicate that the green credit interest subsidies policy has significantly improved the export quality of green products across China’s manufacturing industries. Heterogeneity analysis shows that this policy has had a more pronounced positive impact on green product quality in industries with quality-based competition strategies, in regions with well-coordinated local finance and financial policies, as well as in countries that have concluded environmental clauses with China. Mechanism analysis reveals that, on the export side, the policy enhances green product quality by easing financing constraints, increasing green credit, boosting productivity, and upgrading industrial structures. On the import side, the policy promotes green product quality by expanding the scale, variety, and quality of green intermediate goods. This research offers valuable insights for developing countries aiming to establish export-oriented green transformation and upgrading strategies. Full article

The transition to low-carbon economies presents unique challenges for emerging markets, particularly in developing effective carbon pricing mechanisms that balance environmental objectives with economic development needs. This study examines the ratio legis of carbon pricing policies through a comparative analysis of China and South Korea’s established systems, utilizing the FASTER (Fairness, Alignment, Stability, Transparency, Economic efficiency, Reliability) principles evaluation framework. Using qualitative doctrinal methodology integrated and comparative policy analysis legislative frameworks and market data from 2015–2023, this paper examines Malaysia as a representative case study wherein carbon market initiatives remain in voluntary phase with approximately 150,000 carbon credits traded. The comparative results demonstrate that a choice between China’s intensity-based approach and South Korea’s absolute cap system should be guided by the following: (1) development stage, with industrializing economies benefiting from China’s flexible model; (2) institutional capacity, where limited monitoring capabilities favor phased approaches; (3) economic structure, with emissions-intensive economies requiring growth-accommodating systems; (4) policy landscape complexity; and (5) market size. The research proposes a structured implementation framework for emerging markets, demonstrated through Malaysia’s context, that enables effective emission reduction while maintaining economic competitiveness during the transition to low-carbon economies. Full article

This study endeavors to explore the shock-transmission mechanism between Trade Policy Uncertainty (TPU) and the volatility inherent in the Gulf Cooperation Council (GCC) Islamic stock markets by employing the novel Quantile Vector Auto Regression (QVAR) with “Extended Joint” and “Frequency” domain connectedness technique. Overall findings indicated a U-shaped pattern in the shock-transmission mechanism with the higher TPU shocks transmitted towards Islamic stock market volatility at the extreme quantiles and in the long term. The “Extended Joint” QVAR connectedness approach highlights that, in bearish and moderate-volatility conditions (τ = 0.05, 0.50), diversifying portfolios across less shock-prone equity markets like Qatar and UAE can mitigate risk exposure to TPU shocks. Specific economies receiving higher TPU shocks, like Bahrain, Kuwait, and Saudi Arabia, should implement strategic frameworks, including trade credit insurance and currency hedging, for risk reduction in trade policy shocks during the bearish and moderate-volatility conditions. Conversely, Qatar and Kuwait show the least transmission of error variance from TPU during higher-volatility conditions (τ = 0.95). Moreover, the application of the Frequency-domain QVAR technique underscores the need for short-term speculators to exercise increased vigilance during bearish and bullish volatile periods, as TPU shocks can exert a more substantial influence on the Islamic equity market volatility of Bahrain, Oman, Kuwait, and Saudi Arabia. Long-term investors may need to tailor their asset-allocation strategies by increasing allocations to more stable assets that are less susceptible to TPU shocks, such as Qatar, during bearish (τ = 0.05), moderate (τ = 0.50), and bullish (τ = 0.95) volatility. Full article

Reducing carbon emissions is of immense interest to most modern organizations striving for sustainability. Effective inventory management is crucial for achieving resource optimization and minimizing environmental impact. Very little work has been conducted up to this point on slowly declining, low-quality products with multi-level trade credit rules under the influence of carbon emissions. In this study, an inventory model is tailored specifically for imperfect and gradually deteriorating products with a multi-level trade credit policy. Further, the impact of carbon emissions on the retailer’s ordering strategies is also considered. To determine the optimal policy for supply chain partners, three trade credit instances with seven subcases are taken into consideration. To choose the best scenario out of ten cases, an algorithm is also developed. The model’s validity is illustrated through a numerical experiment and sensitivity analysis. This study is an innovative approach to balancing economic trade credit policy in sustainable supply chain management. Full article

In the process of China’s path to modernization, the concept of harmonious coexistence between man and nature has become increasingly prominent. In the dual context of the development of human society and the improvement of ecological wellbeing, how to reasonably exert environmental regulation policies to actively address the problem of ecological overload has become an important challenge that we need to face urgently. Therefore, based on the panel data of 30 provinces in China from 2005 to 2021, this paper uses the three-dimensional ecological footprint model to evaluate the degree of interference of human activities on the ecological level and selects the difference-in-differences model to examine the impact of external policy shocks, namely, a carbon emissions trading pilot (CETP) policy, on ecological pressure and its transmission mechanism. The results show that moderate government intervention, unified market regulation, and positive industrial response jointly enhance the mitigation effect of CETP on ecological pressure. In areas with strong environmental regulation and a high level of green credit, the incentive effect of the carbon trading mechanism is more significant. In the context of the transformation from industrial civilization to ecological civilization, the findings provide practical guidance and paths for how regions and enterprises can effectively respond to CETP and how governments, markets, and industries can jointly reduce the ecological pressure on the environment. Full article

To promote the low-carbon energy transition, China is implementing renewable energy (RE) development policies such as renewable portfolio standards (RPSs), carbon emissions trading (CET) and China certified emission reduction (CCER) trading. However, using China’s current CET price to accurately reflect market information is difficult, which is not conducive to guiding low-carbon investment. Additionally, as RE power enters the era of grid parity, more revenues are needed to maintain generator operations. Therefore, in this study, we construct a system dynamics model to explore whether and how market mechanisms can optimize the power generation structure, and sensitivity analyses of CCER policy parameters are carried out to identify the impact and scope for improvement. The results show that (1) the market mechanism, especially the RPS mechanism, adjusts the profits of power generators, eliciting a surge in RE generation and optimizing the power generation structure; (2) CET and CCER prices change in the opposite direction of tradable green certificates (TGCs) and show a significant improvement effect on the on-grid electricity price; (3) successful implementation of the CCER mechanism can effectively energize the CET market. A lower CCER benchmark price, higher CCER offset ratio and CET fines can accelerate the growth of CCER and CET prices. Therefore, the government should promote TGC separation from power trading and rationally design CCER policies by lowering the CCER credit ratio, increasing CET fines, and expanding CCER market capacity to ensure that the guiding role of the market mechanism is better utilized. Full article

In striking contrast to the hot debate on the effect of the Green Credit Policy (GCP) on heavy-polluting enterprises (HPEs), little attention has been paid to the policy effect on upstream enterprises of HPEs in the green supply chain. Using the data of China’s A-share listed companies from 2009 to 2020 and regarding the promulgation of GCP as a quasi-natural experiment, we manually collect upstream suppliers providing source control and end-of-pipe treatments to HPEs and investigate the impact of GCP on the green innovation in these suppliers. Findings show that: (i) The GCP promotes green innovation in upstream suppliers of HPEs, especially those providing end-of-pipe treatments. Such findings hold after a series of robustness tests. (ii) Green innovation in these suppliers increases alternatively after promulgating the policy. A higher-than-average growth in green innovation in SOEs (state-owned enterprises) supplying advanced equipment for source control, non-SOEs providing end-of-pipe treatments, large-scale enterprises, as well as suppliers in cities with well-developed finance systems, suggesting that these enterprises are more likely to collaborate with HPEs under credit constraints. (iii) Insight into the potential mechanisms reveals that the credit policy can effectively improve internal environmental concerns and demand-induced trade credits of upstream suppliers to green the supply chain. Full article