AI Powered Analysis
Spatial-temporal evolution analysis of the impact of climate change adaptation policy on industry chain resilience
This study analyzes the spatial-temporal impact of Climate Change Adaptation Policy (CCAP) on urban Industrial Chain Resilience (ICR) in 278 Chinese cities from 2000-2022. It finds CCAP positively influences ICR and has positive spatial spillover effects on neighboring cities. Rural revitalization and the digital economy are identified as mediating mechanisms. The study highlights the need for integrated environmental policies for sustainable urban industrial chains.
Executive Impact & Key Metrics
Unpacking the core quantitative findings and their implications for enterprise strategy.
0.039
Direct ICR Improvement (DID Coeff.)
0.918
Spatial Spillover (Rho Coeff.)
278
Cities Analyzed
23
Years of Data (2000-2022)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Theoretical Framework
Methodology
Key Findings
Policy Implications
Unpacking the Foundations
The theoretical framework emphasizes the conceptual definition and pathways for improvement of Industrial Chain Resilience (ICR). Resilience, originating from physics, signifies a system's ability to return to its original state after an external impact. In economics, it refers to an economy's ability to recover to its pre-shock state after external shocks. ICR specifically focuses on the industrial chain's ability to positively respond to internal and external pressures, maintain dynamic balance, restore its pre-impact state, and achieve transformation and upgrading during dynamic evolution. Unlike supply chain resilience, ICR extends to proactive adaptation to climate change and extreme weather events.
Robust Analytical Approach
Our methodology employs a comprehensive approach to analyze the spatial-temporal evolution of Industrial Chain Resilience (ICR) under the influence of Climate Change Adaptation Policy (CCAP). We first establish an ICR index system using the Hirschman-Herfindahl Index (HHI) for 278 Chinese cities from 2000 to 2022. The implementation of CCAP pilot cities serves as a quasi-natural experiment. We utilize the Spatial Difference-in-Differences (SDM-DID) method to evaluate CCAP's impact, considering spatial autocorrelation. Control variables include Energy Consumption, Urbanization Level, Education Spending, Capital Productivity, and Environmental Regulation. Robustness tests, including traditional DID, inverse matrix, and placebo tests, were conducted to ensure the reliability of our findings. Parallel trend tests confirmed the validity of the DID model assumption.
Core Research Outcomes
The empirical results reveal several critical insights into the relationship between CCAP and ICR. Firstly, CCAP positively and directly enhances urban ICR and exerts a positive spatial spillover effect on the ICR of neighboring cities. Secondly, rural revitalization and the digital economy directly and positively contribute to the improvement of urban ICR, acting as mediating variables. The positive impact of CCAP on ICR is independent of Smart City and Broadband China policies. Thirdly, the study identifies heterogeneous effects: cities with higher levels of science and technology and GDP per capita exhibit stronger improvement and spatial spillover effects on ICR in neighboring cities. Finally, spatial spillover analysis indicates a complex pattern of enhanced, weakened, and renewed positive effects across different distance ranges, underscoring the nuanced regional dynamics of policy diffusion.
Actionable Recommendations
Based on our findings, we propose several policy recommendations for enhancing urban ICR. Governments should integrate CCAP with context-specific rural and digital strategies, emphasizing coordinated implementation. This includes investing in climate-resilient agricultural supply chains, green infrastructure, and promoting digital adaptation technologies in high-emission industries. Policy design should be tailored to regional technological capacity and economic development levels, prioritizing CCAP pilot expansion in high-tech cities and fostering cross-regional partnerships. Lastly, managing spatial spillover and siphoning effects through coordinated regional planning is crucial. This involves supporting adjacent non-pilot cities with shared infrastructure investments and implementing labor mobility agreements to prevent resource over-concentration, promoting balanced and regionally integrated industrial adaptation.
Spotlight Insight: CCAP Positively Influences ICR
+0.039 CCAP's Direct Impact on ICR (Main DID Coefficient)The study finds a statistically significant positive direct impact of Climate Change Adaptation Policy (CCAP) on urban Industrial Chain Resilience (ICR) with a coefficient of 0.039 (Table 2). This indicates that CCAP implementation directly contributes to strengthening the resilience of industrial chains within a city.
Enterprise Process Flow
Climate Change Adaptation Policy (CCAP)
→
Fosters Rural Revitalization (RRI)
→
Promotes Digital Economy (DEL)
→
Enhances Urban Industrial Chain Resilience (ICR)
| Factor | Low Level Impact | High Level Impact |
|---|---|---|
| Technology Level | Weaker direct effect (0.005) and negative spillover (-0.060) | Stronger direct effect (0.052) and no spatial spillover effect (0.252) |
| GDP Per Capita (GPP) | Minimal direct effect (0.004) and negative spillover (-0.035) | Significant direct effect (0.061) and positive spillover (0.191) |
Spatial Dynamics of CCAP: Positive Spillover and Siphoning
The study reveals complex spatial spillover effects of CCAP on ICR. Initially, a positive spillover effect (0-25 km) is observed, indicating benefits for immediately adjacent cities through green infrastructure and industrial clustering. However, a siphoning effect (25-50 km) emerges, where pilot cities draw resources, potentially weakening nearby non-pilot cities. Beyond this, a renewed, though diminishing, positive effect is noted (75-100 km), likely due to broader network effects and policy emulation. This highlights the need for coordinated regional planning to mitigate negative externalities and maximize collective benefits.
Advanced ROI Calculator
Estimate your potential efficiency gains and cost savings by implementing AI-powered resilience strategies.
Estimated Annual Savings
$0
Reclaimed Employee Hours (Annually)
0
Your AI Implementation Roadmap
A phased approach to integrating AI for enhanced industrial chain resilience, derived from the paper's policy recommendations.
Phase 1: Integrate CCAP with Context-Specific Rural & Digital Strategies
Develop and implement policies that co-locate climate change adaptation initiatives with rural revitalization and digital economy strategies. This includes investments in climate-resilient agricultural supply chains, green infrastructure, and promoting digital adaptation technologies in high-emission industries within rural areas.
Phase 2: Tailor CCAP Design Based on Regional Capacity
Customize CCAP strategies according to the local technological capacity and economic development levels of cities. Prioritize CCAP pilot expansion in high-tech cities to accelerate innovation diffusion. Foster cross-regional partnerships between high-resource and low-resource cities through technology transfer and joint industrial clusters.
Phase 3: Manage Spatial Spillover Through Coordinated Planning
Implement coordinated regional planning to mitigate negative siphoning effects and maximize positive spillovers. Support adjacent non-pilot cities with shared infrastructure investments like regional logistics hubs and interoperable industrial data platforms. Establish labor mobility agreements to prevent over-concentration of resources in pilot zones.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat