Search Results (107)

This paper examines the effectiveness of government subsidies in fostering innovation among small and medium-sized enterprises (SMEs), with a particular focus on additionality, crowding-out, and cherry-picking effects. Using the latest national survey data on Chinese high-tech SMEs, we apply robust econometric techniques—including the Heckman selection model, structural equation modeling (SEM), and propensity score matching (PSM)—to address potential selection bias and endogeneity. Our findings reveal that government subsidies positively influence both innovation inputs and outputs, suggesting a predominant additionality effect rather than a crowding-out effect, at least within high-tech SMEs. However, subsidies do not appear to alleviate the financial constraints faced by most SMEs, indicating that they are insufficient as a standalone solution to financing challenges. Furthermore, state ownership enhances input additionality but does not significantly impact output additionality. We also find evidence of cherry-picking in subsidy allocation, with loans exhibiting stronger additionality effects on innovation compared to grants and tax credits, which are more prone to selective intervention. These findings highlight the need for more targeted subsidy policies that prioritize financially constrained firms with high innovation potential while mitigating government selectivity. Our study offers valuable insights for policymakers seeking to design more effective innovation support mechanisms for high-tech SMEs. Full article

We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. Full article

The transition toward a circular water economy addresses accelerating water scarcity and pollution. A PRISMA-2020 systematic review of 50 peer-reviewed articles (January 2018–April 2024) mapped current technologies and management strategies, seeking patterns, barriers, and critical bottlenecks. Bibliometric analysis revealed the following three dominant patterns: (i) rapid diffusion of membrane bioreactors, constructed wetlands, and advanced oxidation processes; (ii) research geographically concentrated in Asia and the European Union; (iii) industry’s marked preference for by-product valorization. Key barriers—high energy costs, fragmented regulatory frameworks, and low social acceptance—converge as critical constraints during scale-up. The following three practical action lines emerge: (1) adopt progressive tariffs and targeted tax credits that internalize environmental externalities; (2) harmonize water-reuse regulations with comparable circularity metrics; (3) create multi-actor platforms that co-design projects, boosting local legitimacy. These findings provide policymakers and water-sector practitioners with a clear roadmap for accelerating Sustainable Development Goals 6, 9, and 12 through circular, inclusive, low-carbon water systems. Full article

This paper examines the impact of carbon capture and storage (CCS) deployment on national carbon intensity (CI) across 43 countries from 2010 to 2020. Using a dynamic common correlated effects (DCCE) log–log panel, we estimate the elasticity of CI with respect to sectoral CCS facility counts within four income-group panels and the full sample. In the high-income panel, CCS in direct air capture, cement, iron and steel, power and heat, and natural gas processing sectors produces statistically significant CI declines of 0.15%, 0.13%, 0.095%, 0.092%, and 0.087% per 1% increase in facilities, respectively (all p < 0.05). Upper-middle-income countries exhibit strong CI reductions in direct air capture (–0.22%) and cement (–0.21%) but mixed results in other sectors. Lower-middle- and low-income panels show attenuated or positive elasticities—reflecting early-stage CCS adoption and infrastructure barriers. Robustness checks confirm these patterns both before and after the 2015 Paris Agreement and between emerging and developed economy panels. Spatial analysis reveals that the United States and United Kingdom achieved 30–40% CI reductions over the decade, whereas China, India, and Indonesia realized only 10–20% declines (relative to a 2010 baseline), highlighting regional deployment gaps. Drawing on these detailed income-group insights, we propose tailored policy pathways: in high-income settings, expand tax credits and public–private infrastructure partnerships; in upper-middle-income regions, utilize blended finance and technology-transfer programs; and in lower-income contexts, establish pilot CCS hubs with international support and shared storage networks. We further recommend measures to manage CCS’s energy and water penalties, implement rigorous monitoring to mitigate leakage risks, and design risk-sharing contracts to address economic uncertainties. 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 recent years, numerous regions worldwide have experienced devastating natural disasters, leading to significant structural damage to buildings and loss of human lives. The reconstruction process highlights the need for a reliable method to document and track the maintenance history of buildings. This paper introduces a novel approach for managing and monitoring restoring interventions using a secure and transparent digital framework. We will also present an application aimed at improving building structures with respect to earthquake resistance. The proposed system, referred as the “Building Ledger Dossier”, leverages a Digital Twin approach applied to blockchain to establish an immutable record of all structural interventions. The framework models buildings using OpenSees, while all maintenance, repair activities, and documents are registered as Non-Fungible Tokens on a blockchain network, ensuring timestamping, transparency, and accountability. A Decentralized Autonomous Organization oversees identity management and work validation, enhancing security and efficiency in building restoration efforts. This approach provides a scalable and globally applicable solution for improving both ante-disaster monitoring and post-disaster reconstruction, ensuring a comprehensive, verifiable history of structural interventions and fostering trust among stakeholders. The proposed method is also applicable to other types of processes that require the aforementioned properties for document monitoring, such as the life-cycle management of tax credits and operations in the financial or banking sectors. Full article

This paper explores cleaner and techno-economically viable solutions to provide electricity, heat, and cooling using green hydrogen (H₂) and green ammonia (NH₃) across the entire decarbonized value chain. We propose integrating a 100% hydrogen-fueled internal combustion engine (e.g., Jenbacher JMS 420) as a stationary backup solution and comparing its performance with other backup technologies. While electrochemical storage systems, or battery energy storage systems (BESSs), offer fast and reliable short-term energy buffering, they lack flexibility in relocation and typically involve higher costs for extended backup durations. Through five case studies, we highlight that renewable-based energy supply requires additional capacity to bridge longer periods of undersupply. Our results indicate that, for cost reasons, battery–electric solutions alone are not economically feasible for long-term backup. Instead, a more effective system combines both battery and hydrogen storage, where batteries address daily fluctuations and hydrogen engines handle seasonal surpluses. Despite lower overall efficiency, gas engines offer favorable investment and operating costs in backup applications with low annual operating hours. Furthermore, the inherent fuel flexibility of combustion engines eventually will allow green ammonia-based backup systems, particularly as advancements in small-scale thermal cracking become commercially available. Future studies will address CO₂ credit recognition, carbon taxes, and regulatory constraints in developing more effective dispatch and master-planning solutions. Full article

The bench-scale demonstration of the UKy-IDEA process for direct air capture (DAC) technology combines solvent-aided CO₂ capture with electrochemical regeneration (ER) through a pH swing process, enabling efficient CO₂ capture and simultaneous solvent regeneration, producing high-purity hydrogen as a valuable co-product. The system shows stable performance with over 90% CO₂ capture efficiency and approximately 80% CO₂ recovery, handling ambient air at 280 L/min. During testing, the unit captured 1 kg of CO₂ over 100 h, with a concentrated CO₂ output purity of around 70%. Operating efficiently at low voltage (<3 V), the system supports flexible and remote operation without AC/DC converters when using intermittent renewable energy. Techno-economic analysis (TEA) and Life Cycle Assessment (LCA) highlight its minimized required footprint and cost-effectiveness. Marketable hydrogen offsets capture costs, and compatibility with renewable DC power enhances appeal. Hydrogen production displacing CO₂ produced via electrolysis achieves 0.94 kg CO₂ abated per kg CO₂ captured. The project would be economic, with USD 26 per ton of CO₂ from the federal 45Q tax credit for carbon utilization, and USD 5 to USD 12 per kg for H₂. Full article

The study constructed a supply chain inventory model for sellers and buyers that integrates payment-time-dependent demand, product defects, misclassification risks, and carbon emission tax considerations. The model was designed to optimize payment time, replenishment time, and order quantities to maximize the seller’s profit per unit time. Theoretical analysis showed that profit exhibited joint concavity with respect to both payment time and replenishment time. An algorithm was also formulated to derive optimal solutions. Finally, numerical experiments and sensitivity analyses validated the model and offered practical insights for managing inventories involving imperfect products. Results indicated that higher responsiveness of demand to payment timing, greater demand coefficients, better product prices, and higher scrap values led to increased seller profits, while greater misclassification, credit default risks, and carbon tax rate reduced it. These insights help decision-makers select suitable parameter values for efficient operations. Full article

This study assesses the energy, economic, and environmental implications of switching Tanzania’s road transport sector to natural gas, which is slowly transitioning. In energy, the main goal is to identify the energy demand for petroleum fuel (diesel and petrol) and natural gas during the transition, while in the economy, the government revenue in the form of taxes for shifted and unshifted vehicles, as well as the loss in government revenue from petroleum fuel revenue post-transition, is assessed. In the environment, carbon emission in terms of carbon dioxide equivalent (CO_2e), carbon tax revenues, and carbon credit revenues post-transition is estimated. The shift involved 10, 20, and 30% of the road vehicle population. The 10, 20, and 30% shift targeted about 142,247, 183,893, and 225,540 vehicles, which in turn dropped diesel and petrol demand by 7 and 3.68%, 7 and 3.8%, and 15 and 7.5%, respectively. In natural gas, the demand started at 0.0916 billion kg and grew exponentially by 200% and later by 300%. The transition has consequences in government revenue, which takes the form of taxes on petroleum products. The shift from 10 to 30% could lead to foregone taxes amounting to Tanzania shilling TZS 0.09, 0.31, and 0.54 trillion (US$ 33,358,680, US$ 11,490,212, and US$ 20,015,208), indicating a tax loss of about 3, 9, and 15%. Contrary, the government may benefit from these losses by lowering the amount of foreign currency necessary for oil importation. In environmental benefits, the 10, 20, and 30% shift could offset approximately 8,959,198.92119, 8,438,863.65528, and 7,918,528.38937 tCO_2e, equivalent to 5.4, 10.97, and 16.47% of the road emissions. The post-transition road emissions might result in a carbon tax revenue of about US$ 71,673,591.37, 67,510,909.24, and 63,348,227.11 per year. The post-transition carbon credit revenue of about US$ 20,813,410.64, 41,626,821.27, and 62,440,231.91 is expected annually. The findings are critical for policy design and promoting a transition in the road transport sector. Full article

Governments need to evaluate technologies generating electricity from different sources; levelised cost of energy (LCOE) is a widely used metric. However, LCOE is weak at comparing disparate technologies, especially where they have different operational lifespans. The discrepancy is demonstrated using UK government data to examine a range of technologies, namely combined cycle generation (natural gas and hydrogen), sustainable renewable technologies along with independent data describing nuclear power and tidal range schemes. Three methods of analysis were used: LCOE, the internal rate of return (IRR), and a novel analysis. A new metric, the sustained cost of energy (SCOE), negates some of the LCOE shortcomings such as the application of discounting. SCOE examines a fixed period of continuous generation, using the lowest common length of operating life of the technologies analysed. It appears to be a useful metric, especially when interpreted with IRR. The analyses produce broadly similar ordering of technologies, but the longer-lasting systems with high initial costings perform better in SCOE. Subsidies, carbon tax, or credit schemes are essential government incentives if net zero emissions targets are to be met without overly burdening consumers with rapidly growing electricity rates. Full article

The integration of non-conventional renewable energy sources (NCRES) plays a critical role in achieving sustainable and decentralized power systems. However, accurately assessing the economic feasibility of NCRES projects requires methodologies that account for policy-driven incentives and financing mechanisms. To support the shift towards NCRES, evaluating their financial viability while considering public policies and funding options is important. This study presents an improved version of the Levelized Cost of Electricity (LCOE) that includes government incentives such as tax credits, accelerated depreciation, and green bonds. We apply a flexible investment model that helps to find the most cost-effective financing strategies for different renewable technologies. To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. The model is tested in a case study in Colombia covering battery storage, large- and small-scale solar power, and wind energy. Results show that combining smart financing with policy support can significantly lower electricity costs, especially for technologies with high upfront investments. We also explore how changes in interest rates affect the results. This framework can help policymakers and investors design more affordable and financially sound renewable energy projects. Full article

Livestock-focused climate-smart (CS) technologies aim to reduce emissions, increase productivity, and improve resilience to climate change. This study reviews CS practices and technologies for cattle production and discusses economic feasibility by exploring the likelihood of consumers’ acceptance of CS beef products and producers adopting these novel technologies on their farms. We identify four key CS technologies and practices cattle farms can adopt: CS farm management (grazing and manure management), methane-reducing feed additives, selective breeding, and genetic engineering. While all these technologies have the potential to reduce methane emissions, practices such as grazing management and using on-farm bio-digesters that do not seemingly alter the animal products are more likely to be accepted by consumers and producers than technologies such as genetic engineering. Although consumers’ willingness to pay for CS beef would be the biggest driver of the on-farm adoption of CS technologies, employing several other market and non-market approaches, such as carbon credits, labeling, tax rebates, subsidies, etc., could help more producers adopt CS technologies. Future studies should focus on understanding the determinants of CS technology adoption and consumer acceptance of CS meat/milk products. Full article

The construction sector plays a pivotal role in sustainability efforts, driving the need for innovative solutions like Building Information Modeling (BIM) to optimize green building design and performance. This study examines the diffusion of BIM functionalities that support sustainability, particularly in energy efficiency, water management, material selection, indoor environmental quality, and green building certification. Using the innovation diffusion theory, the research employs three mathematical models—internal, external, and mixed—to analyze the adoption patterns of BIM for green building applications. Empirical findings reveal that external factors, such as government regulations, financial incentives, and industry trends, significantly influence the diffusion of BIM functions related to environmental performance. The mixed diffusion model demonstrates the highest explanatory power, indicating that both external and internal drivers play a role, particularly in material selection and lifecycle assessment. This study highlights the growing integration of BIM in sustainable construction, reinforcing the need for regulatory support to accelerate adoption. These findings offer valuable insights for researchers, policymakers, and industry professionals, demonstrating how BIM can drive greener practices in the built environment. Policymakers should focus on developing policies and offering incentives such as feed-in tariffs, investment tax credits, and integrating Green BIM requirements into building codes to encourage sustainable construction practices. Also, curricula should be updated to include real-world projects and experiential learning to improve the adoption and efficiency of Green BIM practices. Future research should explore enhanced digital frameworks to further improve BIM’s impact on sustainability and lifecycle optimization. Full article

The United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, solar, clean hydrogen, and advanced nuclear technologies. Modern systems engineering methods and tools can provide deeper insights into these dynamics and future behaviors. This research aims to develop a comprehensive model that captures the main elements and behaviors of new energy technologies within the existing energy system. We hypothesized that the market uptake of novel energy systems is influenced by multiple diverse factors, such as technological learning, availability of resources, and economic incentives; examined the history of electricity generation using land-based wind technologies; and developed a system dynamics model to investigate the relationships between capacity growth and influencing factors, both internal and external. The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. This research aims to support informed decision-making for investments in novel energy systems and aid in developing effective policies for technology deployment. Full article