Results for "Carbon dioxide emission" : Scipedia

Collection of open reports in transport research

India : Energy-Efficient Street Lighting--Implementation and Financing Solutions

Abstract

There has been a clear need for energy-efficient (EE) technologies that can be applicable in the municipal street lighting sector. The objective of this manual is to support the preparation and implementation of street lighting projects in India, using performance contracting and other public private partnership-based delivery approaches. This manual draws upon global best practices, including practices that have been tried and presented within India and South Asia; and draws from their failures and successes to document the major lessons learned. The manual provides a brief overall background of EE in India, the kind of barriers faced in the implementation of EE projects, and the kind of prevalent policy environment for EE in the country. The manual is divided into eight sections. Section one provides an overview of EE street lighting, its components, Indian standards for outdoor lighting, the key lamp technologies, and the potential for intelligent street lighting systems. Section two highlights the financial models for implementation of street lighting projects. Section three describes the essentials of undertaking a detailed energy audit to develop robust baselines. Section four focuses on procurement and contracting. Section five describes methodologies for developing monitoring and verification (M and V) protocols for EE Street lighting projects. Section six brings together the useful tools and matrices in implementation of street lighting projects. Section seven lists the key stakeholders involved in India in such projects and their potential role. Section eight provides details on international and Indian case studies on implementing EE in street lighting and key lessons from these case examples.

Abstract

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Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería

Deep Learning and XAI for Carbon Dioxide Emissions Prediction: Integrating MLP with SHAP and Multi-Policy Scenario Analysis

Abstract

This study is conducted in response to the increasingly prominent climate crisis in contemporary society. It aims to contribute to the growing body of research on the application of deep learning (DL) in environmental sciences and to provide practical guidance for model selection in similar predictive tasks. To this end, the study focuses on carbon dioxide (CO2) emissions prediction, employing Multilayer Perceptron (MLP) models to analyze multi-country panel data. By integrating MLP with explainable artificial intelligence (XAI) techniques, this research not only investigates the underlying mechanisms of various factors influencing CO2 emissions but also quantifies and visualizes the contribution of different driving factors to the prediction outcomes, providing decision support for climate governance strategies. Through an analysis of global panel data, we construct a model incorporating 14 driving factors spanning multiple dimensions, including economic, social, environmental, energy, and technology aspects. To optimize the MLP model, we employ a fivedimensional hyperparameter space comprising hidden layer structure, learning rate, batch size, dropout rate, and training epochs and apply Grid Search for parameter tuning. Experimental results indicate that the MLP model achieves R2 of 0.9951, demonstrating its strong capability in highprecision nonlinear fitting under complex policy scenarios. To further enhance the interpretability of neural networks in CO2 emissions prediction, we introduce SHapley Additive exPlanations (SHAP) to quantify the marginal contributions of different driving factors. This analysis reveals that energy-related features play a dominant role in emission predictions, laying the foundation for scenario analysis and emission reduction policy evaluation. Furthermore, this study incorporates scenario analysis to simulate potential trajectories of CO2 emissions under different policy scenarios, providing a quantitative reference for future emission reduction strategies and environmental governance policies.

Abstract

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