Green construction for low-carbon cities: a review

Chen, Lin; Huang, Lifang; Hua, Jeremy; Chen, Zhonghao; Wei, Lilong; Osman, Ahmed I.; Fawzy, Samer; Rooney, David; Liang, Dong; Yap, Pow‐Seng

doi:10.1007/s10311-022-01544-4

Cited by 120 publications

(37 citation statements)

References 177 publications

(162 reference statements)

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“…Building operations contribute 30% of total global energy consumption and 27% of total carbon emissions from the energy sector over the building life cycle (Daioglou et al 2022 ), with energy consumption vary by building type, such as residential, commercial, and large buildings (Almushaikah and Almasri 2021 ; Chou and Ngo 2016 ). According to Chen et al ( 2023 ), the construction industry is a significant consumer of non-renewable energy and a substantial contributor to greenhouse gas emissions. Construction activities are responsible for 36% of global energy consumption and 39% of global carbon dioxide emissions.…”

Section: Energy Saving In Buildingsmentioning

confidence: 99%

Strategies to save energy in the context of the energy crisis: a review

et al. 2023

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New technologies, systems, societal organization and policies for energy saving are urgently needed in the context of accelerated climate change, the Ukraine conflict and the past coronavirus disease 2019 pandemic. For instance, concerns about market and policy responses that could lead to new lock-ins, such as investing in liquefied natural gas infrastructure and using all available fossil fuels to compensate for Russian gas supply cuts, may hinder decarbonization efforts. Here we review energy-saving solutions with a focus on the actual energy crisis, green alternatives to fossil fuel heating, energy saving in buildings and transportation, artificial intelligence for sustainable energy, and implications for the environment and society. Green alternatives include biomass boilers and stoves, hybrid heat pumps, geothermal heating, solar thermal systems, solar photovoltaics systems into electric boilers, compressed natural gas and hydrogen. We also detail case studies in Germany which is planning a 100% renewable energy switch by 2050 and developing the storage of compressed air in China, with emphasis on technical and economic aspects. The global energy consumption in 2020 was 30.01% for the industry, 26.18% for transport, and 22.08% for residential sectors. 10–40% of energy consumption can be reduced using renewable energy sources, passive design strategies, smart grid analytics, energy-efficient building systems, and intelligent energy monitoring. Electric vehicles offer the highest cost-per-kilometer reduction of 75% and the lowest energy loss of 33%, yet battery-related issues, cost, and weight are challenging. 5–30% of energy can be saved using automated and networked vehicles. Artificial intelligence shows a huge potential in energy saving by improving weather forecasting and machine maintenance and enabling connectivity across homes, workplaces, and transportation. For instance, 18.97–42.60% of energy consumption can be reduced in buildings through deep neural networking. In the electricity sector, artificial intelligence can automate power generation, distribution, and transmission operations, balance the grid without human intervention, enable lightning-speed trading and arbitrage decisions at scale, and eliminate the need for manual adjustments by end-users.

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Section: Energy Saving In Buildingsmentioning

confidence: 99%

Strategies to save energy in the context of the energy crisis: a review

et al. 2023

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“…Waste is a major environmental issue due to its potential to contaminate air, water, and soil. Its generation is mainly attributed to human activities such as industrial production, construction, agricultural activities, pollution emissions, consumption, and waste disposal (Chen et al 2023 ; Ukaogo et al 2020 ). This can lead to environmental pollution, health risks, economic losses, and the loss of resources and costs associated with waste management.…”

Section: Waste Types and Productionmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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“…1 A 2019 report by the Global Energy Agency reveals that construction activity accounts for 36 percent of energy consumption and 39 percent of carbon dioxide emissions, contributing significantly to the global surge in energy consumption and carbon dioxide emissions. 2 In China, construction site energy use for heating and cooling has led to a staggering increase in associated carbon emissions, rising from 1.091 billion tons in 2005 to 2.11 billion tons in 2018, with an alarming annual average growth rate of 5.29 percent. [3][4][5] The traditional heating system in northern China, relying on a central system with coal and gas-fired boilers, significantly raises CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%