Low-Carbon Transformation in Resource-Based Cities by Integrating ChatGPT and ABC Algorithms

By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.Feb 15 2024

In an article published in the journal Humanities & Social Sciences Communications, researchers from the UK and China proposed a novel approach to promote the use of chat generative pre-trained transformer (ChatGPT) and artificial bee colony (ABC) algorithms in the low-carbon transformation and green development of resource-based cities.

They discussed how these methods can provide solutions for improving energy efficiency, reducing carbon emissions, and optimizing urban planning. The research highlighted that the city experienced higher energy efficiency improvements, reduced carbon emissions, and reduced traffic congestion when using ChatGPT or ABC algorithms.

Background

Resource-based cities heavily depend on natural resources, including oil, minerals, or timber, creating a resource-driven economic structure. These cities face challenges, such as high energy consumption, environmental pollution, resource depletion, and social problems. To address these limitations, resource-based cities must make a low-carbon transition, reduce resource dependence, and promote sustainable development and a green economy. However, traditional methods of low-carbon transformation often have problems of low efficiency and high complexity, so new approaches are needed to improve the effect and feasibility of low-carbon transformation.

Deep learning (DL) is a type of machine learning that uses artificial neural networks (ANNs) to learn from data. ANNs are inspired by the human brain and can be used to solve a wide range of problems, such as image recognition, natural language processing, speech recognition, and more. DL can handle unstructured data, such as text and images, and it can automatically learn features from the data without human intervention.

About the Research

In the present paper, the authors investigated the potential of integrating advanced technologies like ChatGPT and ABC algorithms to support green development and low-carbon transformation in resource-based cities. They also evaluated the effectiveness of these technologies across various domains, including policy-making, energy management, smart transportation, urban planning, energy network optimization, and waste management and recycling.

OpenAI’s ChatGPT is a DL-based natural language processing (NLP) model, which has the potential to understand, recognize, and generate human-like or natural language texts. On the other hand, the ABC is an optimization algorithm that replicates bee foraging behavior, offering advantages such as multi-solution and global search capability. Leveraging modern technology, particularly optimization algorithms and DL, the study presented an innovative path for transforming resource-based cities.

The authors adopted a comprehensive approach involving several steps: collecting and preparing relevant input data, including energy consumption, carbon emissions, and traffic flow from resource-based cities, training and tuning models to generate initial solutions, evaluating and selecting the best low-carbon transformation scheme based on optimization results, and implementing and monitoring the chosen scheme in resource-based cities, with feedback provided to further optimize the plan.

The study utilized annual data spanning from 2021 to 2022, sourced from 17 provinces in China. These data were publicly available in the China Statistical Yearbook and the China City Statistical Yearbook. They were then randomly divided into two groups according to specific standards. The first group served as the experimental group, where ABC algorithms or ChatGPT were employed to analyze low-carbon transformation. This group utilized AnyLogic software for simulation purposes. In contrast, the second group functioned as the control group, where neither ChatGPT nor ABC algorithms were employed for low-carbon transformation analysis.

The study employed the directional distance function data envelopment analysis (DDF-DEA) model to address the effect of ChatGPT and the ABC algorithm on low-carbon transformation. This model comprehensively considers multiple input and output indicators, evaluating efficiency by measuring the utilization rate of unit resources.

Research Findings

The outcomes showed that ABC algorithms and ChatGPT have a significant impact on facilitating the transition to low-carbon practices in cities highly dependent on natural resources. Some of the results are as follows:

• The average energy efficiency improvement index for the ChatGPT group was 0.11, whereas, for the ABC algorithm group, it exceeded the control group by 0.02.
• The average carbon emission reduction index for the ChatGPT group was 0.23, while for the ABC algorithm group, it surpassed the control group by 0.04.
• The average traffic congestion alleviation index for the ChatGPT group was 0.15, whereas for the ABC algorithm group, it outperformed the control group by 0.03.

The comprehensive application of ChatGPT and the ABC algorithm could further enhance the low-carbon transformation effect of resource-dependent cities and promote green development. The average total factor productivity of the group using the comprehensive application was 0.86, which was higher than that of the groups utilizing ChatGPT or the ABC algorithm alone, as well as the control group.

Conclusion

In summary, the ChatGPT and ABC algorithms are effective and efficient for low-carbon transformation and green development of resource-based cities. They can help resource-based cities achieve global sustainable development goals. Moreover, they can be helpful for low-carbon policy-making, smart transportation, energy network optimization, energy management, urban planning, and waste management.

The researchers introduced a new methodology for future work in the low-carbon field, offering policy recommendations to the stockholders responsible for decision-making in resource-based cities. Additionally, they acknowledged limitations and challenges, such as the accuracy and reliability of the generated solutions, the parameter setting and computational efficiency of the algorithm, and the robustness and adaptability of the model. Moreover, they suggested some directions for future research, such as improving and optimizing the ABC algorithm and ChatGPT, exploring their application in other fields and scenarios, and comparing their performance with other methods and models.