Enterprise AI Analysis
Research on Carbon Emission Efficiency Based on Super-efficiency SBM and Malmquist Model
In the context of global climate change and China's pursuit of "dual carbon" goals, urban low-carbon transformation is critical. This study introduces an advanced framework for assessing carbon emission efficiency across 42 pilot cities in China from 2010 to 2019.
Traditional DEA models face limitations, particularly in handling undesired outputs like total emissions. This research innovatively constructs an ultra-efficiency SBM-DEA model that incorporates carbon intensity constraints, combined with the Malmquist index. This dual approach provides a comprehensive view of spatio-temporal evolution patterns through static efficiency decomposition and dynamic evolution.
Key Findings & Enterprise Impact
Leverage advanced analytical models to drive sustainability and operational excellence. Here’s what the data reveals:
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Average Carbon Emission Efficiency (2019)
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Annual Total Factor Productivity Growth (Post 2014)
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Cumulative CO2 Emissions/GDP (2010-2019)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Deep Dive
Empirical Findings
Policy Implications
This study employs an advanced, integrated methodology combining Super-efficiency SBM and Malmquist Index models. This approach overcomes limitations of traditional models by incorporating carbon intensity constraints and allowing for dynamic efficiency analysis.
Enterprise Process Flow
Define DMUs, Inputs, Desired & Undesired Outputs
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Apply Super-efficiency SBM-DEA with Carbon Intensity Constraint
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Calculate Static Carbon Emission Efficiency for DMUs
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Apply Global Malmquist Index for Dynamic Efficiency Analysis
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Decompose Total Factor Productivity (ML Index) into EC & BPC
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Derive Policy Recommendations & Transformation Strategies
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The empirical analysis conducted on 42 pilot cities from 2010-2019 reveals significant trends in carbon emission efficiency, regional heterogeneity, and the critical role of technological progress.
54.2%
Current Average Carbon Emission Efficiency (2019) across pilot cities, up from 50.7% in 2010.
106%
Annual Total Factor Productivity Growth since 2014, highlighting the impact of technological progress.
Regional Dynamics: Shenzhen's Success vs. Industrial Bottlenecks
Shenzhen's Leadership: Ranked among the top 9 cities with average efficiency > 0.7, Shenzhen successfully transformed its industrial structure. By developing emerging industries through a "replacing old facilities with new ones" strategy, it increased emerging industries to 37.1% of its economy and reduced unit GDP energy consumption by 45% over ten years.
Industrial Bottlenecks (e.g., Changchun, Taiyuan, Baotou): Several cities, including Changchun (average efficiency < 0.4), face systemic emission reduction challenges. This is largely due to a prominent heavy industrial structure. In 2019, secondary industry proportions in Taiyuan and Baotou reached 43.5% and 51.2% respectively, with high-carbon industries exceeding 60% of manufacturing, significantly hindering efficiency improvement.
The integrated SBM-Malmquist model provides a powerful, systematic framework for policy formulation, enabling cities to assess their carbon emission efficiency and design targeted low-carbon transformation strategies.
The model’s ability to capture regional heterogeneity and dynamic evolution patterns, validated by actual data (e.g., Yangtze River Delta's efficiency improvement through environmental regulation linkage, and the 24.6% reduction in CO2/GDP), makes it an invaluable tool for urban planners and policymakers. It empowers informed decision-making for achieving carbon peak targets and sustainable development.
Future research will further enhance the model's application potential by incorporating external environmental variables and deeper mechanism factors, offering even more precise policy simulation and path optimization capabilities.
Calculate Your Potential Efficiency Gains with AI
Estimate the transformative impact of AI-driven carbon emission efficiency solutions on your enterprise. Adjust the parameters to see your potential savings.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
Our structured approach ensures a seamless integration of AI-powered efficiency solutions into your enterprise, maximizing impact and minimizing disruption.
Phase 1: Initial Assessment & Data Preparation
Comprehensive analysis of existing processes, data sources, and carbon emission profiles to identify key areas for AI intervention. Data collection and cleaning for model readiness.
Phase 2: Model Customization & Training
Tailoring the Super-efficiency SBM and Malmquist models to your specific operational context, including relevant inputs, outputs, and carbon intensity constraints. Training with your historical data.
Phase 3: Pilot Deployment & Validation
Deployment of the AI model in a pilot environment to assess performance, validate efficiency gains, and refine parameters. Iterative adjustments based on real-world results.
Phase 4: Full-Scale Integration & Monitoring
Seamless integration of the validated AI solution into your enterprise systems. Continuous monitoring of carbon emission efficiency, productivity, and other key metrics.
Phase 5: Continuous Optimization & Reporting
Ongoing optimization of the AI models to adapt to changing conditions and new data. Regular performance reporting and insights for strategic decision-making and compliance.
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