Multicollinearity Resolution Based on Machine Learning: A Case Study of Carbon Emissions in Sichuan Province

• URL: http://arxiv.org/abs/2309.01115v2
• Date: Sat, 20 Jan 2024 12:29:57 GMT
• Title: Multicollinearity Resolution Based on Machine Learning: A Case Study of Carbon Emissions in Sichuan Province
• Authors: Xuanming Zhang, Xiaoxue Wang, Yonghang Chen
• Abstract summary: This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan industries using matrix normalization. DBSCAN clustering identified 16 feature classes to objectively group industries. Results showed the second cluster around coal had highest emissions due to production needs.
• Score: 0.6616610975735081
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan industries using matrix normalization. DBSCAN clustering identified 16 feature classes to objectively group industries. Penalized regression models were then applied for their advantages in overfitting control, high-dimensional data processing, and feature selection - well-suited for the complex energy data. Results showed the second cluster around coal had highest emissions due to production needs. Emissions from gasoline-focused and coke-focused clusters were also significant. Based on this, emission reduction suggestions included clean coal technologies, transportation management, coal-electricity replacement in steel, and industry standardization. The research introduced unsupervised learning to objectively select factors and aimed to explore new emission reduction avenues. In summary, the study identified industry groupings, assessed emissions drivers, and proposed scientific reduction strategies to better inform decision-making using algorithms like DBSCAN and penalized regression models.

Related papers

• Machine Learning for Methane Detection and Quantification from Space -- A survey [49.7996292123687]
  Methane (CH_4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO_2) over 20 years. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as Machine Learning (ML) approaches.
  arXiv  Detail & Related papers  (2024-08-27T15:03:20Z)
• Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
  Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
  arXiv  Detail & Related papers  (2024-04-28T05:46:28Z)
• Efficient Strategies on Supply Chain Network Optimization for Industrial Carbon Emission Reduction [0.0]
  This study investigates the efficient strategies for supply chain network optimization, specifically aimed at reducing industrial carbon emissions. We introduce Adaptive Carbon Emissions Indexing (ACEI), utilizing real-time carbon emissions data to drive instantaneous adjustments in supply chain operations.
  arXiv  Detail & Related papers  (2024-04-17T14:53:55Z)
• Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series KMeans Clustering and Multi-Criteria Decision Making [1.6783315930924723]
  This paper introduces a data-driven approach to identify optimal melting patterns in induction furnaces. Using the elbow method, 12 clusters were identified, representing the range of melting patterns. The study successfully identified the cluster with the best performance.
  arXiv  Detail & Related papers  (2024-01-09T14:00:42Z)
• Tabular Machine Learning Methods for Predicting Gas Turbine Emissions [6.488575826304023]
  We evaluate the performance of machine learning models for predicting emissions for gas turbines. We show improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques.
  arXiv  Detail & Related papers  (2023-07-17T10:50:09Z)
• Machine Guided Discovery of Novel Carbon Capture Solvents [48.7576911714538]
  Machine learning offers a promising method for reducing the time and resource burdens of materials development. We have developed an end-to-end "discovery cycle" to select new aqueous amines compatible with the commercially viable acid gas scrubbing carbon capture. The prediction process shows 60% accuracy against experiment for both material parameters and 80% for a single parameter on an external test set.
  arXiv  Detail & Related papers  (2023-03-24T18:32:38Z)
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning [77.62876532784759]
  Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
  arXiv  Detail & Related papers  (2023-02-16T18:35:00Z)
• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model [72.65502770895417]
  We quantify the carbon footprint of BLOOM, a 176-billion parameter language model, across its life cycle. We estimate that BLOOM's final training emitted approximately 24.7 tonnes ofcarboneqif we consider only the dynamic power consumption. We conclude with a discussion regarding the difficulty of precisely estimating the carbon footprint of machine learning models.
  arXiv  Detail & Related papers  (2022-11-03T17:13:48Z)
• Estimating air quality co-benefits of energy transition using machine learning [5.758035706324685]
  Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. We develop a novel and succinct machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition towards a carbon-neutral energy system.
  arXiv  Detail & Related papers  (2021-05-29T14:52:57Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.