Assessing the spatial-temporal environmental efficiency of global construction sector.

The extensive waste emissions and energy consumption in the construction industry has spurred calls for sustainable development reform. Identifying valuable development paradigms through evaluating the spatial-temporal environmental efficiency (EE) of the construction sector offers an effective avenue for practice guidance. However, a comprehensive and detailed global analysis of the construction sector's EE remains lacking.

Scaling of development indicators in countries and its origin.

Population-normalized indicators (e.g., GDP per capita), under the assumption of the indicators scaling linearly with population, are ubiquitously used in national development performance comparison.

Data Science Applications in Circular Economy: Trends, Status, and Future.

The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights.

How does new-type urbanization affect total carbon emissions, per capita carbon emissions, and carbon emission intensity? An empirical analysis of the Yangtze River economic belt, China.

The coordination of the relationship between new-type urbanization (NTU) and carbon emission reduction has become China's primary strategic goal. However, previous studies on this topic mainly examined the unidirectional impact of NTU on carbon emissions, while disregarding their potential relationship. This study establishes an evaluation system for measuring NTU and explores the bidirectional impact between NTU and total carbon emissions (TCE), per capita carbon emissions (PCE), and carbon emission intensity (CEI).

Analyzing the socioeconomic determinants of PM2.5 air pollution at the global level.

Since PM2.5 pollution has jeopardized public health, the research on how ambient fine particulate matter (PM2.5) concentrations are influenced has been increasingly important for the implementation of regional PM2.

Using Deep Learning to Fill Data Gaps in Environmental Footprint Accounting.

Environmental footprint accounting relies on economic input-output (IO) models. However, the compilation of IO models is costly and time-consuming, leading to the lack of timely detailed IO data. The RAS method is traditionally used to predict future IO tables but suffers from doubts for unreliable estimations.

Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach.

Lacking unit process data is a major challenge for developing life cycle inventory (LCI) in life cycle assessment (LCA). Previously, we developed a similarity-based approach to estimate missing unit process data, which works only when less than 5% of the data are missing in a unit process. In this study, we developed a more flexible machine learning model to estimate missing unit process data as a complement to our previous method.

Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning.

Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration.

Analysis on the carbon emission peaks of China's industrial, building, transport, and agricultural sectors.

Carbon emission peak has become a focus of political and academic concern in global community since the launch of Kyoto Protocol. China, as the largest carbon emitter, has committed to reaching the carbon peak by 2030 in Paris Agreement. This ambitious national goal requires the endeavors of individual sectors, particularly those carbon-intensive ones.

Decoupling China's economic growth from carbon emissions: Empirical studies from 30 Chinese provinces (2001-2015).

The world has witnessed unparalleled economic development over the past decades, but accompanied by large amount of carbon emissions, which triggered the global warming. It is critical for the global sustainable development by decoupling economic growth from carbon emissions at country level, specifically for the largest emitter, China. This study conducts a decoupling analysis from the perspective of carbon intensity (CI), per capita carbon emissions (PC) and total carbon emissions (TC) with reference to 30 Chinese provinces, covering the period of 2001-2015.

What are the root causes hindering the implementation of green roofs in urban China?

It is worldwide accepted that green roofs have a variety of environmental, economic, and social benefits. China, which is experiencing rapid urbanization, has great potential to gain the benefits of green roofs, yet which are not commonly seen in the existing or new buildings. Understanding its root causes is important for promoting the larger-scale implementation of green roofs.

A three-step strategy for decoupling economic growth from carbon emission: Empirical evidences from 133 countries.

To decouple the economic growth and carbon emission has been considered imperative to promote low-carbon economy. Nevertheless, previous studies on decoupling analysis between economic growth and carbon emission were contextualized merely in individual countries rather than the globe, which are insufficient for developing the low-carbon economy as a global target. Carbon intensity (CI), carbon emission per capita (CP), and total carbon emission (TC) serve as three important indicators of the status of regional carbon emission, but only decoupling economic growth from TC was analyzed in previous studies.

Critical Success Factors (CSFs) for the Adaptive Reuse of Industrial Buildings in Hong Kong.

With the economic restructuring during the 1980s and 1990s in Hong Kong, most manufacturing plants were relocated to China and many industrial buildings were left neglected or vacant. At the same time, owing to limited land supply, a shortage of affordable housing has been a problem in Hong Kong for many years. Adaptive reuse of industrial buildings may be a way of solving this problem.