Exploring the low-carbon transition pathway of China's construction industry under carbon-neutral target: A socio-technical system transition theory perspective

Impact of policy adjustments on low carbon transition strategies in construction using evolutionary game theory

The construction industry is generally characterized by high emissions, making its transition to low-carbon practices essential for achieving a low-carbon economy. However, due to information asymmetry, there remains a gap in research regarding the strategic interactions and reward/punishment mechanisms between governments and firms throughout this transition. This paper addresses this gap by investigating probabilistic and static reward and punishment evolutionary games. The findings indicate that (1) Probabilistic rewards and penalties policies are more effective during the initial stages of the transition, whereas static mechanisms are more conducive to ensuring long-term stability. (2) The maximum values of rewards and penalties significantly influence the evolution of the low-carbon transition, with higher incentives enhancing motivation and more significant penalties imposing stricter constraints. (3) An increase in the cost of government involvement facilitates the low-carbon transition. (4) The benefits to both government and enterprises are critical in determining the application of static versus probabilistic rewards and penalties. The government may decide to cap probabilistic rewards and penalties by the magnitude of the benefits or adopt static rewards and penalties. This study offers theoretical support and a decision-making framework for developing effective low-carbon policies.

Predicting green technology innovation in the construction field from a technology convergence perspective: A two-stage predictive approach based on interpretable machine learning

The construction industry, as a major global energy consumer and carbon emitter, plays a crucial role in achieving global sustainability. A key strategy for the green transformation of this industry-without compromising development-involves fostering green technology innovation. Nevertheless, existing studies exhibit a notable gap in identifying and evaluating potential green technology innovation opportunities within the construction field, leading to a scarcity of decision-making information for governments and innovation entities during the research and development stage. Recognizing this, our study proposes a two-stage technology opportunity prediction approach based on interpretable machine learning from the perspective of technology convergence. Diverging from previous methods, it not only predicts the probability of technology opportunity occurrence but also forecasts the technical impact of convergence opportunities. By analysing 600,442 patent documents in the green and construction fields, we identify 305 high-potential technology convergence opportunities. Our results reveal that technologies such as carbon capture and storage, pollution alarms, solar energy, forestry techniques, wind energy, energy-saving methods, and waste materials for water treatment have significant potential for convergence with construction technologies. Additionally, we analyse the influencing factors behind these convergence innovations, finding that technical similarity and proximity play crucial roles. These findings provide robust decision support for governments and industry stakeholders in formulating scientifically grounded green technology innovation strategies, thereby accelerating the green transformation of the construction industry and contributing to the goal of sustainable development.

Steel Slag Accelerated Carbonation Curing for High-Carbonation Precast Concrete Development

Steel slag as an alkaline industrial solid waste, possesses the inherent capacity to engage in carbonation reactions with carbon dioxide (CO2). Capitalizing on this property, the current research undertakes a systematic investigation into the fabrication of high-carbonation precast concrete (HCPC). This is achieved by substituting a portion of the cementitious materials with steel slag during the carbonation curing process. The study examines the influence of varying water-binder ratios, silica fume dosages, steel slag dosages, and sand content on the compressive strength of HCPC. Findings indicate that adjusting the water-binder ratio to 0.18, adding 8% silica fume, and a sand volume ratio of 40% can significantly enhance the compressive strength of HCPC, which can reach up to 104.9 MPa. Additionally, the robust frost resistance of HCPC is substantiated by appearance damage analysis, mass loss rate, and compressive strength loss rate, after 50 freeze-thaw cycles the mass loss, and the compressive strength loss rate can meet the specification requirements. The study also corroborates the high-temperature stability of HCPC. This study optimized the preparation of HCPC and provided a feasibility for its application in precast concrete.

A stochastic dynamic programming model for the optimal policy mix of the carbon tax and decarbonization subsidy

Carbon tax and decarbonization subsidy are an effective policy mix in reducing carbon emissions. However, there is a research gap between the deterministic and static analysis related to carbon reduction policy instruments and the dynamic green transition influenced by stochastic factors. This research investigates the optimal dynamic carbon reduction strategies that develop green technologies, increase abatement inputs, and reduce carbon emissions by applying the stochastic optimal control theory. Firms that are incentivized by decarbonization subsidies and regulated by carbon tax choose optimal closed-loop control strategies of abatement inputs to achieve profit-maximizing objectives with carbon reduction constraints. The explicit solutions of the optimal carbon tax and decarbonization subsidy are provided. The simulation results illustrate that the optimal policy mix is feasible in the effective period when the carbon emission decreases significantly, which indicates that the abatement policy mix can effectively promote carbon reduction. Our results reveal that the dynamic optimal policy mix is conducive to achieving carbon abatement goals with capital uncertainty. The government should implement a dynamic carbon tax and decarbonization subsidy policy mix simultaneously associated with optimal closed-loop carbon reduction strategies. Firms with asymmetric decarbonization efficiency can transfer progressively into a cleaner productive pattern.

An integrated economic, energy, and environmental analysis to optimize evaluation of carbon reduction strategies at the regional level: A case study in Zhejiang, China

China plays a crucial role in responding to global climate change. Provinces are the main sources of energy consumption and greenhouse gas emissions in China's economic and social development. However, it is still unclear how to achieve dual-carbon goals by formulating and implementing local policies to adapt to climate change. In this study, we take Zhejiang Province in China as the research object, based on the LEAP (Low Emissions Analysis Platform) model to construct four social scenarios under different policies, comprehensively considering regional economic characteristics, population, and energy consumption patterns. The results show that to achieve Zhejiang Province's goal of carbon peaking by 2030 while maintaining steady economic growth, additional measures are required to reduce energy consumption intensity or improve the power generation structure. Otherwise, energy demand will increase to 228.06 million tonnes of coal equivalent and carbon emissions will be 487.76 million tonnes in 2050. Moreover, developing clean energy and promoting CCUS technology can continuously reduce carbon emissions to 293.59 and 210.76 million tonnes respectively. The economic viability of CCUS power generation is contingent upon the development of carbon taxes in the future. Once the growth rate reaches 7.2%, power cost will be 167.77 billion RMB and CCUS will become economically advantageous in 2050.

The government-production nexus of energy efficiency in China's construction industry: regional difference and factor analysis

For decades, the construction industry has contributed significantly to China's economic growth. The heavy energy consumption inevitably leads to the release of large amounts of carbon emissions. Improving energy efficiency has been a crucial solution for mitigating the environmental impacts while boosting its green economy in the construction industry. Measuring the energy efficiency in the construction industry considering the quality of government sector is still limited. Using panel provincial data in China from 2011 to 2020, this paper proposes a two-stage dynamic data envelopment analysis (DEA) framework integrating the government sector with the production sector in the construction industry, and calculates energy efficiency. The spatial Durbin model is used to analyze the driving forces of energy efficiency. The research findings include (1) the energy efficiency in the eastern region is higher than that in the central and western regions. The mean values of energy efficiency in the eastern, central, and western regions are 0.42, 0.34, and 0.37. (2) Even though governance efficiency is lower than production efficiency, there is a positive correlation between governance efficiency and production efficiency with a correlation coefficient of 0.48. Improving governance efficiency is a significant step to increase the production efficiency and further increase energy efficiency of the construction industry. (3) Digital transformation has a positive effect on governance efficiency but has no effect on production efficiency. The government-production nexus framework provides implications for clarifying the role of government intervention in improving energy efficiency.

Effect of Carbonation Treatment on the Properties of Steel Slag Aggregate

Steel slag is the waste slag generated after steel smelting, which has cementitious activity. However, untreated steel slag can damage the integrity of steel slag concrete due to its harmful expansion. This study prepared porous aggregates by mixing powdered steel slag, fly ash, and cement and carbonated them with CO2 under high pressure conditions (0.2 MPa). The effect of carbonation on the performance of steel slag aggregate was studied using volume stability and crushing value. The effect of different carbonation conditions on the products was studied using X-ray diffraction (XRD) and thermogravimetric (TG) analyses, and the carbon sequestration efficiency of steel slag under different treatment methods was quantitatively evaluated. The research results indicate that untreated steel slag was almost completely destroyed and lost its strength after autoclave curing. With the increase in temperature and carbonation time, the performance of steel slag aggregate gradually improved and the pulverization rate, expansion rate, and crushing value gradually decreased. According to the experimental results of XRD and TG, it was found that the reaction between f-CaO (free CaO) and CO2 in steel slag generated CaCO3, filling the pores inside the aggregate, which was the internal reason for the improvement of aggregate performance. After comparison, the best carbonation method was maintained at 55 °C for 72 h. After carbonation, the steel slag aggregate had a pulverization rate of 2.4%, an expansion rate of 0.23%, a crushing value of 23%, and a carbon sequestration efficiency of 11.27% per unit weight of aggregate.