Boqiang Lin

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The deepening financialisation of commodities has intensified the complex macro‐linkages within emerging energy markets. This paper investigates the dynamic connectedness among China's crude oil futures (INE), interest rates and exchange rates from a term‐structure perspective (level, slope and curvature). Employing the Nelson–Siegel model and a time‐varying parameter vector auto‐regression (TVP‐VAR) framework based on daily data from March 2018 to June 2025, we analyse the spillover effects of term structure factors to trace their interconnections across short‐term, medium‐term and long‐term horizons. Empirical results reveal that the exchange rate acts as a primary risk transmitter, whereas the interest rate functions as a shock absorber. Notably, system connectivity intensifies during crises, with interest rates reversing roles to become a net transmitter during the Russia–Ukraine conflict. Additionally, the INE market remains a passive price taker, heavily constrained by macro‐financial shocks. Crucially, geopolitical risk and energy price uncertainty exert significant non‐linear moderating effects on this cross‐market network. These findings provide implications for managing cross‐market risks in emerging derivatives markets.

Purpose: This paper investigates risk spillovers between US and Chinese agricultural futures markets under asynchronous trading and evaluates the driving role of US-China tensions. Design/methodology/approach: We apply the R2-decomposed connectedness framework to measure contemporaneous and lagged multi-layer spillovers between the two markets, and use a GARCH–MIDAS model to assess the impact of the news-based UCT Index. Findings: The main findings are threefold. First, contemporaneous connectedness between US and Chinese agricultural commodities is the dominant component, while lagged connectedness (asynchronous trading and delayed adjustment) remains economically important, and US agricultural commodities are a persistent net transmitter at all layers of the risk network. Second, UCT has a significant state-dependent influence on the systemic risk of these agricultural commodities and becomes a strong positive driver in high-UCT regimes. Third, a minimum connectedness portfolio based on R2 decomposition achieves more effective risk-adjusted performance than portfolios built on traditional connectedness models. Originality/value: This study helps to clarify the complex pattern of risk transmission and price discovery between the two markets and provides practical tools for measuring and managing risk spillovers and market vulnerability.

New energy enterprises (NEEs) are facing supply chain risks arising from geopolitical conflicts and global trade frictions. Supply chain concentration (SCC), a core metric quantifying the reliance on core upstream suppliers and downstream customers, has become a key focus of NEEs’ operational risk management. The rapidly evolving artificial intelligence (AI) technologies is providing critical impetus for the transformation toward supply chain diversification. Yet few literature has studied the relationship of AI transformation and NEEs’ SCC, which may hinder the deep integration of AI into supply chain management. Based on panel data of China’s new energy A-share listed enterprises from 2007 to 2023, this paper employs a fixed-effects model to examine the relationship between AI transformation and SCC, as well as the underlying transmission mechanisms and heterogeneous effects. The findings show that: First, AI transformation significantly reduces the SCC of NEEs. This conclusion remains valid after a series of endogeneity and robustness tests. Second, mechanism analysis shows that AI transformation decreases SCC through promoting absorptive capability, innovative capability and adaptive capability. Third, heterogeneity analysis reveals that AI transformation decreases SCC more significantly for firms located in regions with stronger digital infrastructure and for firms in the maturity or decline stages. This study provides empirical evidence for the strategic deployment of AI to optimize supply chain structures in NEEs and offers decision-making guidance for policymakers to promote an intelligent transformation of the new energy industry.

The pressing demand for decarbonization within the context of economic development has generated substantial opportunities for the rapid growth of renewable energy. Consequently, further research is essential to promote the advancement of the renewable energy sector, with a crucial emphasis on understanding the barriers firms face when entering this field. This study investigates the aforementioned issue by examining the relationship between business expansion and inefficient investment among China's listed renewable energy companies. First, we compared phased investment (PI-type) and merger-and-acquisition (M&A-type) renewable energy enterprises. The preliminary results revealed the presence of entry barriers: the inefficient investment of PI-type enterprises exhibited an inverted U-shaped relationship with the expansion of their renewable energy business, whereas there was no significant relationship for M&A-type enterprises. Furthermore, we examined the underlying causal relationships by accounting for industry trends and constructing instrumental variables. Additionally, we conduct a moderation effect analysis on the nonlinear relationship based on traditional theories of enterprise business expansion and market competition. By comparing PI-type with M&A-type enterprises, we provide new empirical evidence supporting traditional theoretical frameworks. Finally, considering the unique characteristics of the renewable energy industry, we examine how three types of government roles mitigate entry barriers. These conclusions serve as a reference for governments to formulate policies for renewable energy development and help enterprises understand the trade-offs in transitioning into emerging industries.

The precise forecasting of total electricity generation and consumption can provide data support for framing the sustainable development planning of a developing nation. The study framework is composed of two phases: electricity generation and carbon mitigation framework and structure and logical procedures. This study develops decomposition and Long-range Energy Alternatives Planning system methods and analyzes electricity and CO ₂ emission mitigation scenarios for 2025–2035 under Energy Vision 2035, including policy implementation, carbon management, and intensity effects. Six main driving factors, including low-carbon structure, electricity generation intensity, energy security, energy intensity, carbon productivity, and CO ₂ emissions for four decades, are investigated. The results show that (i) low-carbon electricity presents a rising trend in the current phase, which means the renewable electricity structure is undergoing development; (ii) the carbon production factor shows the Pakistan's economy is improving due to substantial advancements in low-carbon electricity generation, mitigating CO ₂ emissions; (iii) electricity demand scenarios show that renewable, hydro, and nuclear electricity are major contributing resources and are lowering CO ₂ emissions; (iv) CO ₂ emission mitigation scenarios from 2025 to 2035 indicate that CO ₂ will be cut by 63.40%, showing that low-carbon electricity resources may produce relative efficiencies in the electricity sector; and, finally, and (v) the development of low-carbon electricity generation and decarbonization could be ensured by regulating carbon mitigation policies and technical development.