Xun Zhou

1. Carbon dioxide emissions (CO2) remain one of the most pressing global environmental challenges, largely driven by rapid growth in energy consumption (Zhou et al., 2022). In response, countries a...

Generative artificial intelligence serves as an information medium that dynamically bridges tourists with destination cultural narratives. Drawing on the media ecology theory, this study employs a sequential mixed-methods design to explore how generative artificial intelligence adoption impacts tourists’ reactions during cultural tourism. Thematic analysis of 38 interviews was conducted to develop the conceptual model, subsequently validated through structural equation modeling using a survey of 733 tourists. The results demonstrate that tourists’ attitudes toward generative artificial intelligence adoption, shaped by its personalized and anthropomorphic features, manifest as trust and attachment, which mitigate tourists’ perceived information cocoon and subsequently promote destination-related behavioral intentions. Tourists’ self-efficacy in managing misinformation strengthens the negative relationship between their generative artificial intelligence attachment and evaluation of information cocoons. This study contributes new knowledge to the field of generative artificial intelligence adoption in cultural tourism and offers an enlightening idea for the advancement of the media ecology theory.

Net metering and time-of-use pricing play a vital role in the widespread adoption of distributed renewable energy (DRE). However, existing mechanisms may be overly weighted toward renewable energy development and fail to accommodate the large-scale adoption of DRE. This paper examines how net metering and time-of-use pricing can adapt to the mass DRE adoption through a literature review. We first provide a conceptual modeling framework for making DRE investment decisions and then report the main findings from reviewing existing literature on the roles of the net metering and time-of-use pricing on DRE adoption. It is found that net metering may lead to overcompensation or an unfair cross-subsidy effect; multiple rule-based compensations, such as avoided costs, value of solar rate and levelized cost of energy, have emerged as alternative approaches for optimizing net metering design. The effects of time-of-use pricing indicate that setting the right timing in time-of-use pricing design can enhance both the economic performance and self-consumption value of the DRE technologies.

Explaining the secular stagnation of productivity growth is a widely recognized challenge to economists and policymakers. One potentially important explanation without much attention concerns the ongoing low‐carbon transition. This paper explores whether considering greenhouse gas emissions can explain productivity stagnation in OECD countries. We propose a quantile shadow‐price Fisher index to gauge green total factor productivity (TFP) based on the newly developed penalized convex quantile regression approach. The quantile shadow‐price Fisher index requires neither the real price data nor an ad hoc choice of quantiles and allows the quantiles to move in the inter‐period sample. An empirical application to 38 OECD countries during 1990–2019 demonstrates that the measured productivity growth is considerably higher when the GHG emissions are accounted for. For countries that have reduced GHG emissions most actively, the average green TFP growth rate could double the conventional TFP growth. The impacts of ignoring human capital and different representations of fixed capital on green TFP growth are also discussed explicitly.

The visitor economy is responsible for a substantial percentage of the global carbon footprint. The mechanisms used to decarbonize it are insufficient, and the industry is relying on carbon trading with substandard credits that allow businesses to outsource the responsibility to decarbonize. We aim to transform carbon markets, help finance climate investments, and support decarbonization strategies. We identify and define the problem, outline the components and their interactions, and develop a conceptual model to transform carbon markets. The new, blockchain-based Carbon Tokenomics Model rolls out a decentralized database to store, trade, and manage carbon credits, with the goal of enabling sustainable climate finance investment. We outline the criteria needed for an industry-wide carbon calculator. We explain the process needed to increase rigor in climate investments in the visitor economy and introduce a delegated Proof of Commitment consensus mechanism. Our inclusive and transparent model illustrates how to reduce transaction costs and how to build consumer and industry trust, generating much-needed investments for decarbonization.