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Stakeholder interests are complex, sensitive and highly uncertain and may influence the development of net zero carbon building (NZCB). However, this study aims to conduct a systematic literature review to explore the stakeholder interests towards achieving NZCB.

A total of 62 articles were identified from the Scopus database and thoroughly reviewed to extract relevant information on stakeholders' interest towards achieving NZCB.

A total of 28 stakeholder interests influencing the development of NZCB were identified from the literature and were classified into six major groups according to their uniqueness (economic, social, environmental, technological, political, regulatory and legal).

The findings of this study provide insight into the specific stakeholder interests towards achieving NZCB. Thus, the findings of this study could serve as a guide for future research, policy formulation and implementation to expedite the practice of building towards net zero carbon (NZC). Empirical studies are suggested in future studies to test and consolidate the theoretical claims of this study.

This paper undertakes a comprehensive systematic review of studies on stakeholder interests towards achieving NZCB, which is the least investigated in the literature.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

The purpose of this paper was to prepare a ternary hierarchical rough particle to accelerate the anti-corrosive design for coastal concrete infrastructures.

A kind of micro-nano hydrophobic ternary microparticles was fabricated from SiO₂/halloysite nanotubes (HNTs) and recycled concrete powders (RCPs), which was then mixed with sodium silicate and silane to form an inorganic slurry. The slurry was further sprayed on the concrete surface to construct a superhydrophobic coating (SHC). Transmission electron microscopy and energy-dispersive X-ray spectroscopy mappings demonstrate that the nano-sized SiO₂ has been grafted on the sub-micron HNTs and then further adhered to the surface of micro-sized RCP, forming a kind of superhydrophobic particles (SiO₂/HNTs@RCP) featured of abundant micro-nano hierarchical structures.

The SHC surface presents excellent superhydrophobicity with the water contact angle >156°. Electrochemical tests indicate that the corrosion rate of mild steel rebar in coated concrete reduces three-order magnitudes relative to the uncoated one in 3.5% NaCl solution. Water uptake and chloride ion (Cl^-) diffusion tests show that the SHC exhibits high H₂O and Cl^- ions barrier properties thanks to the pore-sealing and water-repellence properties of SiO₂/HNTs@RCP particles. Furthermore, the SHC possesses considerable mechanical durability and outstanding self-cleaning ability.

SHC inhibits water uptake, Cl^- diffusion and rebar corrosion of concrete, which will promote the sustainable application of concrete waste in anti-corrosive concrete projects.

The purpose of this study is to explore the coopetition relationships between platform owners and complementors in complementary product markets. Drawing on the coopetition theory, the authors examined the evolutionary trends of the coopetition relationships between platform owners and complementors and explore the main influence factors.

The authors used Lotka–Volterra model to analyze the coopetition relationship between platform owners and complementors, including the evolutionary trends as well as the results. Considering the feasibility of sample data collection, simulation is used to verify the effects of different factors on the evolution of coopetition relationships.

The results show that there are four possible results of the competition in the complementary products market. That comprises “winner-take-all for platform owners,” “winner-take-all for complementors,” “stable competitive coexistence” and “unstable competitive coexistence,” where “stable competitive coexistence” is the optimal evolutionary state. Moreover, the results of competitive evolution are determined by innovation subjects’ interaction parameters. However, the natural growth rate, the initial market benefits of the two innovators and the overall benefits of the complementary product markets influence the time to reach a steady state.

The study provides new insights into the entry of platform owners into complementary markets, and the findings highlight the fact that in complementary product markets, platform owners and complementors should seek “competitive coexistence” rather than “winner-takes-all.” Moreover, the authors also enrich the coopetition theory by revealing the core factors that influence the evolution of coopetition relationships, which further enhance the analysis of the evolutionary process of coopetition relationships.

The literature has dealt with the relationship between board characteristics (BC) and firm performance (FP) on a large scale. However, it yielded inconsistent results. Thus, this paper aims to examine the indirect relationship between BC and FP through the mediating role of the capital structure (CS).

This study used a sample of 528 non-financial companies listed on Bursa Malaysia from 2015 to 2019. Also, a two-step system generalised method of moments estimation technique was applied.

The results show that board diversity and the frequency of board meetings positively affect financial performance, and it is negatively influenced by board turnover, size and independence. Also, the results indicate a positive relationship between the independence of the board and all CS variables. Importantly, the findings support the policy-setting role of the board of directors where CS (measured by total debt and short-term debt) suppresses some governance mechanisms’ detrimental effect on FP. Hence, the board of directors, apart from the monitoring function, introduce various policies (financial and non-financial) that enhance the overall performance of companies.

These results are consistent with the agency’s perspective that management practices in selecting the optimal capital reduce agency costs and improve performance. The findings contribute to developing a broader theoretical framework that accounts for the policy-setting role of the board of directors. The current study model of corporate governance offers insight for policymakers into the role of corporate governance other than monitoring functions in organisations and how CS should be taken into consideration with corporate governance and FP association.

The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

Despite the global shift toward smart building features and technologies, the level of awareness among stakeholders in Nigeria’s construction sector remains unclear, limiting engagement with these innovations. This study examines the awareness of smart building features and technologies, providing insights to address knowledge gaps and improve understanding within the sector.

This study adopted the quantitative research approach, using a questionnaire survey to obtain data from construction stakeholders that were purposively selected in Lagos State and Abuja, Nigeria. The collected data were analyzed using various statistical tools such as frequencies, percentiles, mean item scores, standard deviation and the Mann–Whitney U test.

From the result of the analysis, the study concluded that the most cognizant smart building features and technology were security doors, escalators and lifts, solar panels and energy-saving equipment, fire alarms, heating, ventilation, air and conditioning.

This study provides insights into the awareness of smart building features and technologies among Nigerian construction stakeholders, bridging theory and practice. It informs policy development, enhances professional knowledge and promotes educational initiatives. Its findings support sustainable construction efforts, potentially improving societal attitudes and quality of life.

This study uniquely explores the level of awareness of smart building features and technologies among clients and professionals in Nigeria’s construction sector. Identifying existing knowledge gaps provides critical insights that can guide efforts to enhance understanding and foster deeper engagement with these innovations.

With the rise in international construction projects, addressing the psychological health concerns of expatriate construction professionals (EXCPs) has become of paramount importance. Therefore, this study aims to identify and evaluate effective intervention measures to promote optimal psychological health and well-being in overseas working environments.

Intervention measures were identified through a comprehensive literature review. Subsequently, using a case study of Chinese international contractors, data were collected via a quantitative survey administered to EXCPs holding managerial positions in international construction projects. Fuzzy synthetic evaluation was employed to analyze the data.

The findings identified 23 intervention measures and 4 constructs, among them, measures focused on enhancing organizational justice and training practices offer the greatest potential for enhancing the psychological health and well-being during international assignments. Furthermore, the analysis showed that the overall criticality of the intervention measures to the international contractors is significant, suggesting that if put into practice, the psychological health and well-being of EXCPs can be significantly enhanced.

While prior research has explored intervention measures suitable for construction professionals working on domestic projects, little attention has been paid to EXCPs in the context of international construction projects. This study offers an initial exploration of the most crucial intervention measures and provides a valuable resource for policymakers and practitioners seeking to establish a psychologically healthy working environment for international construction projects.

This study explores whether targeted microcredit can effectively alleviate households’ multidimensional relative poverty (MdRP) in rural China in the new era following the poverty elimination campaign and discusses it from a gendered perspective.

This study applies a fixed-effects model, propensity score matching (PSM) and two-stage instrumental variable method to two-period panel data collected from 611 households in rural western China in 2018 and 2021 to explore the effects, mechanisms and heterogenous performance of targeted microcredit on households’ MdRP in the new era.

(i) Targeted microcredit can alleviate MdRP among rural households in the new era, mainly by reducing income and opportunity inequality. (ii) Targeted microcredit can promote women’s empowerment, mainly by enhancing their social participation, thereby helping alleviate households’ MdRP. The effect of the targeted microcredit on MdRP is more significant in medium-educated women households and non-left-behind women households. (iii) The MdRP alleviation effect is stronger in villages with a high degree of digitalization.

Learn from the experience of targeted microcredit. Accurately identify poor groups and integrate loan design into financial health and women empowerment. Particularly, pay attention to less-educated and left-behind women households and strengthen coordination between targeted microcredit and digital village strategies.

This study clarifies the effect of targeted microcredit on women’s empowerment and households’ MdRP alleviation in the new era. It also explores its various effects on households with different female characteristics and regional digitalization levels, providing ideas for optimizing microcredit.