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This study aims to evaluate the characteristics of climate change in Yunnan minority areas and identify an effective path to promote sustainable livelihoods based on climate change.

Taking Yunnan Province as an example, based on the expansion of the traditional sustainable livelihood framework, the authors constructed a system dynamics (SD) model of sustainable livelihood from the six subsystems of natural, physical, financial, social, human and cultural and tested the accuracy and effectiveness of the model with data from Cangyuan County. By adjusting these parameters, five development paths are designed to simulate the future situation of the livelihood system and determine the optimal path.

Climate change has exacerbated the vulnerability of people’s livelihoods. In future, each of the five development paths will be advantageous for promoting sustainable livelihoods. However, compared with Path I (maintaining the status quo), Path III (path of giving priority to culture) and Path IV (path of giving priority to economic development) have more obvious advantages. Path II (path of giving priority to people’s lives) gradually increases the development rate by promoting people’s endogenous motivation, and Path V (path of coordinated development) is better than the other paths because of its more balanced consideration.

The analytical framework of sustainable livelihoods based on the characteristics of minority areas is broadened. By constructing a SD model of the livelihood system, the limitations of traditional static analysis have been overcome and a development path for promoting sustainable livelihoods through simulation is proposed. This study offers a theoretical framework and reference method for livelihood research against the backdrop of climate change and a decision-making basis for enhancing climate adaptability and realizing sustainable livelihoods.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

The purpose of this paper is to analyze manufacturers' production decisions and governments' low-carbon policies in the context of influencer spillover effects.

This paper investigates the impact of the social influencer spillover effect on manufacturers' production decisions when they collaborate with intermediary platforms to sell products through marketplace or reseller modes. Game theory and static numerical comparison are used to analyze our models.

Firstly, under low-carbon policies, the spillover effect does not always benefit manufacturer profits and changes non-monotonically with an increasing spillover effect. Secondly, in cases where there are both a carbon emission constraint and a spillover effect present, if either the manufacturer or intermediary platform holds a strong position, then marketplace mode benefits manufacturer profits. Thirdly, regardless of business mode used when environmental damage coefficient is high for products; government should implement cap-and-trade regulation to optimize social welfare while reducing manufacturers’ carbon emissions.

This study offers theoretical and practical research support to assist manufacturers in optimizing production decisions for compliance with carbon emission limits, enhancing profits through the development of effective influencer marketing strategies, and providing strategies to mitigate carbon emissions and enhance social welfare while sustaining manufacturing activities.

This paper addresses the limitations of prior research by examining how the social influencer spillover effect influences manufacturers' business mode choices under government low-carbon policies and analyzing the social welfare of different carbon emission restrictions when such spillovers occur. Our findings provide valuable insights for manufacturers in selecting optimal marketing strategies and business modes and decision-makers in implementing effective regulations.

This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability.

A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed.

Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm's profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm's profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above.

This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability.

The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data.

For an entrant platform in the on-demand service market, choosing an appropriate employment model is critical. This study explores how the entrant optimally chooses the employment model to achieve better performance and investigates the optimal pricing strategies and wage schemes for both incumbent and entrant platforms.

Based on the Hotelling model, the authors develop a game-theoretic framework to study the incumbent's and entrant's optimal service prices and wage schemes. Moreover, the authors determine the entrant's optimal employment model by comparing the entrant's optimal profits under different market configurations and analytically analyze the impacts of some critical factors on the platforms' decision-making.

This study reveals that the impacts of the unit misfit cost of suppliers or consumers on the pricing strategies and wage schemes vary with different operational efficiencies of platforms. Only when both the service efficiency of contractors and the basic employee benefits are low, entrants should adopt the employee model. Moreover, a lower unit misfit cost of suppliers or consumers makes entrants more likely to choose the contractor model. However, the service efficiency of contractors has nonmonotonic effects on the entrant's decision.

This study focuses on an entrant's decision on the optimal employment model in an on-demand service market, considering the competition between entrants and incumbents on both the supplier and consumer sides, which has not been investigated in the prior literature.

This paper proposes an intact framework for building life cycle energy estimation (LCEE), which includes three major energy sources: embodied, operational and mobile.

A systematic review is conducted to summarize the selected 109 studies published during 2012–2021 related to quantifying building energy consumption and its major estimation methodologies, tools and key influence parameters of three energy sources.

Results show that the method limitations and the variety of potential parameters lead to significant energy estimation errors. An in-depth qualitative discussion is conducted to identify research knowledge gaps and future directions.

With societies and economies developing rapidly across the world, a large amount of energy is consumed at an alarming rate. Unfortunately, its huge environmental impacts have forced many countries to take energy issues as urgent social problems to be solved. Even though the construction industry, as the one of most important carbon contributors, has been constantly and academically active, researchers still have not arrived at a clear consensus for system boundaries of life cycle energy. Besides, there is a significant difference between the actual and estimated values in countless current and advanced energy estimation approaches in the literature.

Information technology-enabled gig platforms connect freelancers with consumers to provide short-term services or asset sharing. The growth of gig economy, however, has been accompanied by controversy, and, recently, food delivery platforms have been criticized for using data-driven techniques to set strict delivery time limits, resulting in negative externality. This study aims to provide managerial implications on the decisions of delivery time and subsidy for food delivery platforms.

The authors develop an analytical framework to investigate the optimal delivery time and subsidy provided to delivery drivers to maximize the gig platform's profit and compare the results with those of a socially optimal outcome.

The study reveals that it is optimal for the platform to shorten the delivery time and raise the subsidy when the food price becomes higher; nevertheless, the platform should shorten the delivery time and lower the subsidy in response to a higher delivery fee. Increases in the food price or delivery fee have non-monotonic effects on the number of fulfilled orders and the platform's profit. In addition, the authors solve the socially optimal outcome and find that a socially optimal delivery time is longer than the platform's preferred length when the delivery fee is high and the negative externality is strong.

The food delivery platform's optimal decision on delivery time is derived after taking negative externality into account, which is rarely considered in the prior literature but is a practically important problem.

This study aims to identify the pandemic’s impact on house rents by applying a rental gradient analysis to compare the pre-and post-COVID-19 periods in Auckland. The micro-level household census data from the Integrated Data Infrastructure of Statistics New Zealand is also applied to scrutinise this WFH trend as a robustness check.

Since the outbreak of COVID-19, work-from-home (WFH) and e-commerce have become much more common in many cities. Many news reports have contended that households are leaving city centres and moving into bigger and better houses in the suburbs or rural areas. This emerging trend has been redefining the traditional theory of residential location choices. Proximity to central business district (CBD) is no longer the most critical consideration in choosing one’s residence. WFH and e-commerce flatten the traditional bid rent curve from the city centre.

The authors examined micro-level housing rental listings in 242 suburbs of the Auckland Region from January 2013 to December 2021 (108 months) and found that the hedonic price gradient models suggest that there has been a trend of rental gradient flattening and that its extent was almost doubled in 2021. Rents are also found to be increasing more in lower-density suburbs.

The results imply that the pandemic has accelerated the trend of WFH and e-commerce. The authors further discuss whether the trend will be a transient phenomenon or a long-term shift.

Suppose an organisation is concerned about productivity and performance issues due to a companywide ability to WFH. In that case, some standard key performance indicators for management and employees could be implemented. Forward-thinking cities need to focus on attracting skilful workers by making WFH a possible solution, not by insisting on the primacy of antiquated nine-to-five office cultures.

WFH has traditionally encountered resistance, but more and more companies are adopting WFH policies in this post-COVID era. The early rental gradient and the micro-level household data analysis all confirm that the WFH trend is emerging and will likely be a long-term shift. Instead of resisting the change, organisations should improve their remote work policies and capabilities for this WFH trend.

So far, empirical studies of post-COVID urban restructuring have been limited. This study aims to empirically test such an urban metamorphosis by identifying the spatial and temporal impacts of COVID on house rental gradients in the Auckland Region, New Zealand. The authors apply rental gradient analysis to test this urban restructuring hypothesis because the method considers the spatial-temporal differences, i.e. a difference-in-differences between pre-and post-pandemic period against the distance measured from the city centre. The method can control for the spatial difference and the endogeneity involved.