Carbon price prediction considering climate change: A text-based framework

Xie, Qiwei; Hao, Jingjing; Li, Jingyu; Zheng, Xiaolong

doi:10.1016/j.eap.2022.02.010

Cited by 33 publications

(12 citation statements)

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“…Hao and Tian ( 2020 ) developed a hybrid forecasting framework considering multiple influence factors that affect the carbon trading price. Xie et al ( 2022 ) incorporated the variable of climate change into the prediction model and found better accuracy in the experiments on Hubei and Guangdong carbon markets. Zhang and Xia ( 2022 ) enhanced the prediction performance of the European Union Allowance (EUA) (EUR/ton) closing price for future contract transactions by adding the variable constructed from online carbon market news.…”

Section: Literature Reviewmentioning

confidence: 99%

The role of online news sentiment in carbon price prediction of China’s carbon markets

Liu

Ying

2023

Environ Sci Pollut Res

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Carbon trading as a vital tool to reduce carbon dioxide emissions has developed rapidly in recent years. Reasonable prediction of the carbon price can improve the risk management in the carbon trading market and make healthy development of the carbon trading market. This paper aims to enhance the predictive performance of carbon price in the China’s carbon markets, especially the China’s national carbon market, by adding the online news sentiment index which is a kind of unconstructed data, to a deep learning model using traditionally constructed predictors innovatively. Long short–term memory (LSTM) network was applied as the primary model to predict carbon price and random forest as the additional experiment to validate the effectiveness of online news sentiment. The results in the China’s national carbon market and Hubei pilot carbon market both proved that the model including the sentiment index performed better than the model does not, and the improvement was significant.

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Section: Literature Reviewmentioning

confidence: 99%

The role of online news sentiment in carbon price prediction of China’s carbon markets

Liu

Ying

2023

Environ Sci Pollut Res

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“…However, before diving into different analysis techniques, it is vital for air quality and climate-related data to be processed beforehand [49]- [53]. Pre-processing serves the essential purpose of data preparation, cleaning and enhancement, ensuring a higher analysis quality [50]- [53]. Some works in the literature use pre-defined pre-processing techniques [49], [53], [54], such as fuzzy logic or ANFIS.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Climate Change and Air Quality Over the Last Decade: Evidence From News and Weather Data Processing

Văduva,

Munteanu,

Oprea

et al. 2023

IEEE Access

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Climate change is a phenomenon that is sometimes denied or trivialized. However, in recent years, we have faced extreme phenomena such as fires, floods, excessive temperatures, etc. which affect our physical and mental condition and the environment, often leading to significant material damage. To understand these problems and highlight the meteorological and phenomenological changes encountered in the last decade, time series were web-scraped and analyzed from several open data sources: weather news broadcast in Romania, air quality, temperature, etc. The extraction and organization of data recorded between 2009 and 2023 are formulated as a framework that can be reproduced and replicated to continue the monitoring. The exploratory analysis of the categorical and numerical data highlights intricate patterns and correlations within meteorological conditions across regions and seasons. From temperature trends to air quality fluctuations, the study underscores the dynamic interplay of weather phenomena, paving the way for informed forecasting and deeper climate research. At the same time, data processing includes Latent Dirichlet Allocation, K-prototype clustering analysis, in addition to K-means clustering with dimensional reduction techniques, all of which are employed to further reveal the extreme phenomena in news and regions with higher occurrence. Therefore, in this paper, we propose a data processing framework for multiple datasets and analytics, extracting valuable information on climate change and identifying the exposed regions to extreme phenomena. INDEX TERMSclimate change; news; web scraping; NLP; data analysis; data clustering

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“…(2) Energy prices Changes in oil, natural gas, and coal price lead to changes in their consumption, which, in turn, affects carbon price volatility. 40 In this study, Brent and WTI crude oils are selected as oil prices because the two major crude oil markets in the world can well reflect the crude oil market. For the natural gas price, that on the New York Mercantile Exchange is chosen due to the price limit of natural gas by the Chinese government.…”

Section: Influencing Factor Data Collectionmentioning

confidence: 99%

“…Historical prices are found to have a strong correlation with predicted prices. Trading volumes not only directly reflect carbon market activities but also contain much information related to carbon market operations. Energy pricesChanges in oil, natural gas, and coal price lead to changes in their consumption, which, in turn, affects carbon price volatility 40 . In this study, Brent and WTI crude oils are selected as oil prices because the two major crude oil markets in the world can well reflect the crude oil market.…”

Section: Data Collection and Evaluation System Constructionmentioning

confidence: 99%

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A novel interpretable model ensemble multivariate fast iterative filtering and temporal fusion transform for carbon price forecasting

Wang

Zhong

Kang

et al. 2022

Energy Science & Engineering

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The accurate forecasts of carbon prices can help policymakers and enterprises further understand the laws of carbon price fluctuations and formulate related policies and investment strategies. Nowadays, many carbon price prediction models have been proposed. However, some models ignore the time–frequency relationship when considering exogenous variables and fail to measure their importance to the forecasting results, leading to unsatisfactory results. Therefore, this study proposes a novel hybrid model for carbon price forecasting on the basis of advanced multidimensional time series decomposition techniques and interpretable multifactor models. In the proposed model, multivariate fast iterative filtering is used to decompose carbon price and its exogenous variable sequence into several intrinsic mode functions, which can overcome the nonlinearity and nonstationarity of carbon prices and obtain their intrinsic characteristics. Meanwhile, temporal fusion transform (TFT) is used to interpret predictions for multivariate time series. TFT is a new attention‐based deep learning model combining high‐performance multihorizon prediction and interpretability and can adaptively select the optimal features for carbon price prediction. Five carbon markets in Guangdong, Beijing, Shanghai, Hubei, and Shenzhen are selected for experimental studies. Empirical results indicate that the proposed model outperforms the compared benchmark models in all performance metrics. In the interpretable output of TFT, the prediction of the high‐frequency part requires the participation of exogenous variables and has a long time dependence; for the middle and low‐frequency part, only using the carbon price itself and a short time step can lead to good results. This finding can inform future research on carbon price forecasting and help policymakers.

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Carbon price prediction considering climate change: A text-based framework

Cited by 33 publications

References 50 publications

The role of online news sentiment in carbon price prediction of China’s carbon markets

The role of online news sentiment in carbon price prediction of China’s carbon markets

Understanding Climate Change and Air Quality Over the Last Decade: Evidence From News and Weather Data Processing

A novel interpretable model ensemble multivariate fast iterative filtering and temporal fusion transform for carbon price forecasting

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