Industrial carbon emission forecasting considering external factors based on linear and machine learning models

自回归积分移动平均 平均绝对百分比误差 Lasso(编程语言) 温室气体 水准点(测量) 计算机科学 线性回归 支持向量机 自回归模型 计量经济学 时间序列 机器学习 数学 人工神经网络 地理 万维网 生物 生态学 大地测量学
作者
Ye Liang,Pei Du,Shubin Wang
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:434: 140010-140010 被引量:24
标识
DOI:10.1016/j.jclepro.2023.140010
摘要

Accurate forecasting of carbon emissions has become a critical task for the government to formulate effective policies and sustainable development. However, previous studies have mainly focused on large-scale carbon emissions forecasting, while urban-level carbon emission forecasting is equally important but rarely covered. In this study, we propose a novel carbon emission forecasting framework combining linear and machine learning models that considers both time dynamics and external influences. To improve the accuracy and explanatory power of the proposed model, we first introduce twelve initial influencing factors by considering the urban development, economic development, industrial energy consumption, and demographic factors. And then Lasso regression algorithm is adopted to filter out the indicators with poor predictive power. Finally, a combined prediction model by integrating Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) models is established to capture linear and nonlinear features, respectively. The simulation results show that compared with benchmark models, the proposed model indicates stronger prediction performance with a Mean Absolute Percentage Error (MAPE) of 0.096 and a R-squared (R2) of 97.5%. In addition, six future development scenarios, including carbon emission projections for industrial growth and environmental protection factors, are also performed in this study to provide recommendations for carbon emission reduction programmers and related policy formulation. In conclusion, the forecasting framework proposed in this research can help to identify the key factors affecting carbon dioxide emissions and provide a quantitative reference for carbon dioxide emission reduction.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
米九完成签到,获得积分10
2秒前
zhao完成签到,获得积分10
5秒前
普鲁卡因发布了新的文献求助10
5秒前
zj完成签到,获得积分10
11秒前
蓝橙完成签到,获得积分10
12秒前
16秒前
GD88完成签到,获得积分10
17秒前
糟糕的梨愁完成签到,获得积分10
18秒前
莫西莫西完成签到 ,获得积分10
19秒前
小趴蔡完成签到 ,获得积分10
21秒前
唐唐发布了新的文献求助10
21秒前
飘逸剑身完成签到,获得积分10
24秒前
airtermis完成签到 ,获得积分10
24秒前
gfasdjsjdsjd完成签到,获得积分10
25秒前
25秒前
杨宁完成签到 ,获得积分10
25秒前
MchemG应助transition采纳,获得20
26秒前
科研通AI2S应助科研通管家采纳,获得10
27秒前
lxy发布了新的文献求助10
29秒前
gfasdjsjdsjd发布了新的文献求助10
30秒前
JCao727完成签到,获得积分10
30秒前
30秒前
31秒前
OAHCIL完成签到 ,获得积分10
32秒前
lixueao发布了新的文献求助10
33秒前
无辜的行云完成签到 ,获得积分0
36秒前
FIN应助gfasdjsjdsjd采纳,获得20
38秒前
今后应助gfasdjsjdsjd采纳,获得10
38秒前
排骨炖豆角完成签到 ,获得积分10
40秒前
一叶知秋应助大橙子采纳,获得10
42秒前
kk完成签到,获得积分10
45秒前
敏感春天完成签到,获得积分10
45秒前
凶狠的期待完成签到,获得积分10
47秒前
自然函发布了新的文献求助30
47秒前
JF123_完成签到 ,获得积分10
48秒前
量子星尘发布了新的文献求助10
48秒前
50秒前
杨召发布了新的文献求助10
51秒前
didi完成签到,获得积分10
51秒前
keyby发布了新的文献求助10
53秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038128
求助须知:如何正确求助?哪些是违规求助? 3575831
关于积分的说明 11373827
捐赠科研通 3305610
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022