Behind the Paper

Forecast data of provincial carbon emissions in China from 2025 to 2035: based on ARIMA-BP model

Published in Earth & Environment and Computational Sciences

Go to the profile of Sanglin Zhao
Sanglin Zhao and Sanglin Zhao
2 contributors
Forecast data of provincial carbon emissions in China from 2025 to 2035: based on ARIMA-BP model
Like
Go to the profile of Yijia Li
Go to the profile of Saeed Shojaee Barjoee

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

Share with...

...or copy the link

This is a representation of how your post may appear on social media. The actual post will vary between social networks

China is an important contributor to global carbon emissions. Accurately estimating carbon emissions is crucial for reducing carbon emissions in accordance with the United Nations Sustainable Development Goals and China's carbon neutrality strategy. This study selected energy consumption data from Chinese provinces from 2000 to 2021, calculated carbon emissions using the carbon emission factor method, and then predicted carbon emissions data for 2022-2035 using the ARIMA-BP model. This dataset can be used to describe the spatiotemporal evolution trend of carbon emissions in China. This study can provide policy basis and wisdom for China's carbon reduction efforts.

Go to the profile of Sanglin Zhao

Contributors

Author

Go to the profile of Sanglin Zhao

Sanglin Zhao

Dr., Hunan University of Finance and Economics

Contributing author

Go to the profile of Sanglin Zhao

Sanglin Zhao

Dr., Hunan University of Finance and Economics

Multiple Contributors
Sanglin Zhao and Sanglin Zhao

Contributors

Author

Go to the profile of Sanglin Zhao

Sanglin Zhao

Dr., Hunan University of Finance and Economics

Contributing author

Go to the profile of Sanglin Zhao

Sanglin Zhao

Dr., Hunan University of Finance and Economics

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Sign In Register

Follow the Topic

Artificial Intelligence
Mathematics and Computing > Computer Science > Artificial Intelligence
Earth Sciences
Physical Sciences > Earth and Environmental Sciences > Earth Sciences

Recommended Content

Cookies

We use cookies to ensure the functionality of our website, to personalize content and advertising, to provide social media features, and to analyze our traffic. If you allow us to do so, we also inform our social media, advertising and analysis partners about your use of our website. You can decide for yourself which categories you want to deny or allow. Please note that based on your settings not all functionalities of the site are available.

Further information can be found in our privacy policy.

Cookie Control

Customise your preferences for any tracking technology

The following allows you to customize your consent preferences for any tracking technology used to help us achieve the features and activities described below. To learn more about how these trackers help us and how they work, refer to the cookie policy. You may review and change your preferences at any time.

See the full cookie policy See the full privacy policy
More information

These trackers are used for activities that are strictly necessary to operate or deliver the service you requested from us and, therefore, do not require you to consent.

More information

These trackers help us to deliver personalized marketing content and to operate, serve and track ads.

More information

These trackers help us to deliver personalized marketing content to you based on your behaviour and to operate, serve and track social advertising.

More information

These trackers help us to measure traffic and analyze your behaviour with the goal of improving our service.

More information

These trackers help us to provide a personalized user experience by improving the quality of your preference management options, and by enabling the interaction with external networks and platforms.