Technological Forecasting and Social Change最新文献

The digital global value chains (DGVCs) embody the two major characteristics of the current era: globalization and digitalization. The DGVCs has a major impact on global economic development, but few studies have explored its impact on the energy system. We divide digital global value chain participation (DGVCP) into forward digital global value chain participation (FDGVCP) and backward digital global value chain participation (BDGVCP) and analyze their effect on energy resilience by employing panel data from 49 countries from 2007 to 2019. Our benchmark regression results provide empirical evidence of the negative effects of DGVCP, showing that FDGVCP and BDGVCP reduce energy resilience. Moreover, the negative impact of FDGVCP and BDGVCP is stronger in a transparent institutional environment. The results remain robust after causal identification. The mechanism analysis results show that FDGVCP and BDGVCP influence energy resilience through the scale channel, structure channel, and technology channel. Our research reveals that the DGVCs have a negative impact on energy resilience, contrasting with the conclusions of previous mainstream studies. Our findings underscore some important implications for avoiding the negative effects of DGVCs and enhancing energy resilience.

Climate policy is essential for fostering sustainable development and addressing climate-related issues, while climate policy uncertainty (CPU) introduces complex challenges in providing a consistent framework for action. In this study, we delve into the spillover dynamics among China's domestic CPU, foreign CPU, physical assets, and financial assets across various market scenarios, through a quantile-based connectedness method and evidence from China. The findings reveal: (1) The spillover dynamics are asymmetric across quantiles and time, and the COVID-19 pandemic influences the total connectedness. (2) The complexity of spillover patterns in physical assets is likely contingent upon market conditions, policy sources, asset types, and industrial-chain positions, while the spillovers of financial assets are quantile-dependent and time-varying, with the agricultural equity market showing relative resilience to the pandemic. (3) There is no significant Granger causality in quantiles between domestic CPU and foreign CPU under the entire sample period, and the rankings regarding the spillovers from CPUs to assets vary across market conditions. Implications for policymakers, investors, and firms are provided.

Biopharmaceutical firms' opportunities depend on the accurate estimation of highly acclaimed clinical trial projects duration. To understand how forecasts of the duration differ from the actual duration, this study uses Construal Level Theory (CLT), which places the future estimates on the different levels of distance in estimation errors. This article explores (a) whether the estimated project duration is indeed longer than the actual duration of the project, and (b) how it differs when considering foreign versus domestic projects. A dataset of 24,953 technology projects in two locations make two discoveries. First, the actual duration of completed projects (past) is likely to be 0.26 times longer than the estimated duration of future projects, implying that project sponsors tend to underestimate the project duration. Second, the foreign versus domestic projects likely to be 0.26 times longer than the domestic sponsors projects. Third, foreign and past project further increases the duration by 0.40 times compared to the domestic and past project. The study resolves the duration issue, contributes to the CLT framework by supporting the psychic distance principle and construal levels. It reveals potential errors in the estimation of the unknown future based on the known past.

Considering the technological and institutional nature, along with the inherent complexity, of the rural digital governance context, a data-driven institutional pressure transmission (D-IPT) mechanism is developed in this study. This mechanism aims to facilitate effective regulation of villagers' behaviours, thereby improving rural digital governance performance. Firstly, we proposed a conceptual model based on complex adaptive systems theory and institutional theory to demonstrate how this mechanism can ensure an improvement of the emergent rural digital governance performance. Subsequently, we translated the conceptual model into a computational representation, thereby constructing a theory-informed simulation model. A behavioural dataset consists of 1,255,206 rural households from 119 villages over a span of 18 months was used to instantiate and validate the simulation model based on a specific scenario. Then, we designed five simulation experiments to investigate interventions aimed at harnessing the D-IPT mechanism more effectively to improve the rural digital governance performance. The results show that interventions targeting interaction efficiency, interaction frequency, and institutional environment configuration significantly affect the role of the D-IPT mechanism in enhancing rural governance performance. Finally, we discussed the theoretical and practical implications of the D-IPT mechanism.

Destructive extreme weather caused by climate change causes severe biodiversity loss, prompting us to accelerate the energy transition to net-zero emissions to achieve green sustainability goals. Thus, this paper aims to examine the relationship between biodiversity risks and the energy transition. We choose to use four indicators—carbon emissions (COE), the energy financial market (COE), climate policy uncertainty (CPU), and the world production industry (WPI)—to characterize the energy transition process and use newly constructed global biodiversity attention indicator (GBAI) to characterize biodiversity risks. We find a significant feedback relationship between the GBAI and the COE (CPU), which also confirms the close connection between climate and biodiversity. Second, we find a long-term feedback mechanism between the GBAI and COE (CPUWPI), but only the GBAI and the COE have a feedback mechanism in the short term. Finally, the impact of the GBAI on the COE (CPU) occurs over a short period, while the impact of the COE (WPI) on the GBAI occurs over multiple periods. Therefore, these detectable feedback relationships prompt us to adjust short- and long-term environmentally friendly policies related to climate and carbon emissions.

Green growth has become a critical driver in sustainability science. However, attaining a steady state of green growth can be challenging in the face of exogenous economic shocks. As such, this study assesses the time-varying relatedness among the USA's green economy (OMXGE), carbon markets (CCAI), green energy (ECO), natural resource markets (SPNGR), and sustainability index (DJS) considering multiple economic traumatism (i.e., the Brexit, COVID-19, Russia-Ukraine conflict). By deploying the quantile VAR (QVAR) and “Wavelet Local Multiple Correlations (WLMC)” approaches, we find: 1) Without the quantile effect, we note strong total interconnectedness among the designated series, which is close to 93 %. 2) From the QVAR findings, we note a high shock-receiving tendency in ECO and CCAI during the Brexit referendum, the first wave of COVID-19, and the Ukraine-Russia conflict, making these markets attractive for further investment during turbulent times. 3) DJS and OMXGE are temporary shock receivers and predominant shock transmitters, indicating that the green economy and sustainability indicators are not robust enough to absorb shocks during economic shocks. 4) Wavelet coherence findings further validate that the green economy is not a safe haven asset, whereas the carbon market has a strong shock absorption capacity and can provide strong hedging support. The underlying findings reveal that Washington's policy toward a renewable-based equity market should be more liberal to ramp up investment in renewables.

To understand the slow adoption of blockchain technology by organisations, we conduct a systematic literature review of adoption factors using a mixed-methods approach. Using thematic analysis, 880 factors are identified and grouped into 29 themes, which offer a comprehensive overview of the literature. Using statistical analysis, the identified factors are dissected into technological (T), organisational (O), and environmental (E) dimensions (the TOE framework). Themes are further classified as barriers (B), enablers (En), and ambiguous (A) to describe a firm's readiness for blockchain adoption (the BEnA framework). We emphasise the multidimensionality of adoption factors across the TOE dimensions and the conditionality of adoption enablers across the BEnA dimensions. Analysis of research trends shows that recent blockchain adoption literature has focused on elaborating upon existing research themes (involution) rather than on developing new themes (evolution). Based on our analyses, we propose future research directions, including scrutinising the interdependence and multidimensionality of blockchain adoption factors, further examining factors with conditional or unclear effects on adoption, and broadening the contextual, temporal, and theoretical aspects of blockchain adoption research.

Green development has become the primary development concept and an imperative development approach under the requirements of sustainable development of the global economy. Using a dataset with A-share listed firm observations from 2008 to 2021 in China, we examine the impact of Communist Party of China (CPC) organization in privately owned firms (POEs) on corporate green innovation. We find that the embedding of CPC organization promotes corporate green innovation of POEs. The results still holds after the robustness test. Mechanism analysis shows that the promotion effect of CPC organization comes from its ability to alleviate financing constraints and help enterprises to establish the concept of green development, enhancing private enterprises' ability and willingness of POEs to promote green innovation. Heterogeneity analysis shows that the promotion effect of CPC organization is more pronounced in regions with a low degree of marketization, non-heavily polluting industries and companies without individual-level political connections. While enriching the literature in the field of corporate green innovation and embedding theory, we also provide a theoretical basis and policy reference for promoting corporate green transformation and green innovation.

Climate change and global warming have induced a dynamic shift in extreme rainfall patterns, leading to nonstationary behaviour. This alteration in behaviour challenges conventional hydrologic design, which assumes stationarity and can yield misleading outcomes. This study aims to address nonstationarity by modelling distribution parameters with covariates. Utilizing a 70-year (1951–2020) high-resolution India Meteorological Department (IMD) gridded dataset, extreme annual rainfall across diverse Indian cities was extracted and modelled. Previous research and goodness-of-fit tests favour the Generalized Extreme Value (GEV) distribution for modelling extremes. This study incorporates indices like Nino3.4, dipole mode index, global and local temperature, CO2, and time to characterize nonstationarity in extreme annual rainfall, leveraging climate cycles and global warming. Performance assessment employs the Akaike information criterion, Bayesian information criterion and Likelihood ratio test, while quantile reliability is evaluated through confidence intervals (CIs). Findings reveal widespread nonstationary trends in most grid points, translating to wider CIs in estimated quantiles for chosen non-exceedance probability and covariates in fitted models. Generally, nonstationary conditions are associated with broader confidence bands in return levels, highlighting nonstationary model weaknesses compared to stationary models. However, the results showed that the rainfall extremes follow a nonstationary pattern. Hence, there is a strong need to develop nonstationary models of low uncertainty.

The development of information and communications technology has resulted in changes in electrical safety inspection. Electrical safety inspectors plan to introduce smart devices that detect causes of extensive electrical fires in real time without problems of power disconnections and on-site visits. However, smart inspection is expected to encounter acceptance issues because users are unaccustomed to it and obligated to pay additional costs for device installation and data communications. This study aims to investigate smart inspection acceptance using survey data. A hierarchical Bayesian model is employed to explore the effects of users' characteristics on its acceptance. The respondents prefer attributes of smart inspection to those of the prevailing method of on-site inspection, excluding monthly inspection costs. They prefer more frequent and extensive inspections, and prefer to avoid power disconnections and physical interaction. Accordingly, the government should inform users that smart inspection is convenient and accurate. It is also attractive to users who wish to avoid on-site visits because of privacy issues. The acceptance rate can increase if the government reduces inspection costs using the existing smart devices and communications infrastructure, and offers real-time electrical safety information using smart phones and in-home displays.