Intelligent comprehensive evaluation system using artificial intelligence for environmental evaluation

Liu, Wenwen; Zhao, Jingfeng; Du, Longbo; Padwal, Harshila H.; Vadivel, Thanjai

doi:10.1016/j.eiar.2020.106495

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…Despite the attempts that try to tackle sustainability problems by adopting a more systemic and interdisciplinary perspective, several other studies address the AI and digitalisation related methods to advance specific SD challenges such as urban water management (Goralski & Tan, 2020 ; Xiang et al, 2021 ), disaster detection (Al Qundus et al, 2020 ; Alizadeh & Nikoo, 2018 ; Sublime & Kalinicheva, 2019 ), recycling and waste sorting (Bircanoğlu et al, 2018 ), poverty assessment and monitoring (Alsharkawi et al, 2021 ; Jean et al, 2016 ), food consumption and sustainability (Abdella et al, 2020 ), sustainable cities/urban ecosystems (Goddard et al, 2021 ; Ilieva & McPhearson, 2018 ; Majumdar et al, 2021 ; Vinuesa et al, 2020 ), threat to biodiversity (Jensen et al, 2020 ), health-related issues (Pirouz et al, 2020 ), smart sustainable agriculture (Alreshidi, 2019 ; Boev et al, 2020 ), climate action (Balogun et al, 2020 ; Fuso Nerini, Slob, et al, 2019 ; Fuso Nerini, Sovacool, et al, 2019 ; Huntingford et al, 2019 ), environmental evaluation (Liu et al, 2021 ), and enterprise management for SD (Kościelniak et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…Case studies found digitalisation to support climate change adaptation and preparedness (Balogun et al, 2020 ). Comparison of AI algorithms for sustainability assessment (Bienvenido-Huertas et al, 2020 ), environmental evaluation (Liu et al, 2021 ), and text mining analysis (Sebestyén et al, 2020 ) all show the power of digitalisation for SD assessment and management. As earlier stated, AI can be used to achieve sustainability and the SDGs.…”

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

“…While the total number of case studies on this topic in the first 6 years was only 155, the corresponding number in the more recent 6 years so far has already skyrocketed to 1982. Characteristic of bi-directional transmission of data and electricity among utility companies and power consumers, the digitisation of existing grid with smart meters and networked sensors has made the gird increasingly “smarter,” i.e., more intelligent, efficient, effective and reliable by transmitting both power and data among power generating utilities, prosumers and organisations, and power consuming residents and businesses (Liang et al, 2020 ; Liu et al, 2021 ). Hybrid-cloud-based big data processing, IoT technologies, advanced analytics with AI algorithms such as Blockchain and deep machine learning, and abundant communication standards used to send data from one point to another, as well as digital twins in the digital transformation of utilities and in Industry 4.0 further accelerate the intelligent metamorphosis of the grid (Ahmad et al, 2020 ; Hasankhani et al, 2021 ; Talaat et al, 2020 ).…”

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

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Deploying digitalisation and artificial intelligence in sustainable development research

Filho

Yang

Eustachio

et al. 2022

Environ Dev Sustain

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Many industrialised countries have benefited from the advent of twenty-first century technologies, especially automation, that have fundamentally changed manufacturing and industrial production processes. The next step in the evolution of automation is the development of artificial intelligence (AI), i.e. intelligence which is demonstrated by machines and systems, which cannot only perform tasks but also work synergistically with humans and nature. Intelligent systems that can see, analyse situations and respond sensitively to real-time cues, from human gestures and facial expressions to pedestrians crossing a busy street, will reshape transportation, precision agriculture, biodiversity conservation, environmental modelling, public health, construction and manufacturing, as well as initiatives designed to promote prosperity on Earth. This paper explores the connections between AI systems and sustainable development (SD) research. By means of a literature review, world survey, and case studies, ways in which AI can support research on SD and, inter alia, contribute to a more sustainable and equitable world, are identified.

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Section: Resultsmentioning

confidence: 99%

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

See 1 more Smart Citation

Deploying digitalisation and artificial intelligence in sustainable development research

Filho

Yang

Eustachio

et al. 2022

Environ Dev Sustain

View full text Add to dashboard Cite

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“…Establishing a scientific environmental assessment system is necessary for economic and social development [44]. As shown in Figure 2, the current technical standard of EIA exhibits the following characteristics.…”

Section: Analysis Of Technical Support Indexesmentioning

confidence: 99%

The Feasibility of Constructing a Greenhouse Gas Emission Assessment System in China and Its Legislative Path

Zhao

Tian

2023

Sustainability

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In order to achieve the ambitious carbon peak and neutrality targets, China is in the process of revising Environmental Impact Assessment Law. This study is devoted to the proposition of how to incorporate activities regulating greenhouse gas emissions into the legal system of Environmental Impact Assessment (EIA). This study is inspired by the Embedding Theory, which provides the explanatory framework that allows economic activities to be regulated by political and social norms. This framework requires a relevance and cost analysis of the three indicators required to regulate economic activities: administrative support, technical support, and legal support. The results reveal that the structure of China’s Greenhouse Gas Emissions Assessment (GGEA) system and that of the EIA system are closely related and that the generation and transaction costs involved in the process of institution embedding are low. This result satisfies the requirements of the Embedding Theory for achieving institutional coupling. In conclusion, this study provides an outlook on the direction of the revision of China’s EIA Law from the perspective of building a rule of law pathway for GGEA with “4EM-3CS-3RC” as the core.

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“…Liu et al proposed a method for calculating the correlation score between the composition being tested and the topic based on rich features. Because this method requires training for various composition topics in order to obtain various features, it has some practical limitations [6]. Random indexing is a technique proposed by Onds and Gurcik.…”

Section: Introductionmentioning

confidence: 99%

Construction of Intelligent Evaluation Model of English Composition Based on Machine Learning

Wang

2022

Mobile Information Systems

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To automatically grade compositions, automatic composition grading primarily employs statistics, mathematical analysis, machine learning, natural language processing, and other technologies. This paper presents a machine learning-based intelligent evaluation model for English composition. As a result, this paper proposes a word vector grouping-based text content representation method and a vector space model-based text content representation method. The Word2Vec model is first trained with words, then used to generate a test model of word vectors, with the statistical information of the corresponding words in each category serving as content text features. The results show that when features based on word vector clustering are added to the content text, the effect of each model improves significantly, especially the maximum entropy model, which improves by 0.048. The XGBoost model has also seen a significant improvement, going from 0.771 to 0.803. The test corpus is made up of 100 articles chosen at random from the corpus, and the test set is checked for errors. The rate of accuracy is 68 percent. The conclusion demonstrates that the model presented in this paper can help promote English teaching reform and quality-oriented higher education while also easing the burden on teachers and students.

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Intelligent comprehensive evaluation system using artificial intelligence for environmental evaluation

Cited by 14 publications

References 21 publications

Deploying digitalisation and artificial intelligence in sustainable development research

Deploying digitalisation and artificial intelligence in sustainable development research

The Feasibility of Constructing a Greenhouse Gas Emission Assessment System in China and Its Legislative Path

Construction of Intelligent Evaluation Model of English Composition Based on Machine Learning

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