Life cycle assessment of greenhouse gas emissions with uncertainty analysis: A case study of asphaltic pavement in China

China currently accounts for over 25% of global anthropogenic carbon emissions. Cutting down the carbon emissions of the construction industry is a major task for achieving the carbon neutrality goal in China. However, research on benchmarks and probability distribution models for China's embodied carbon emissions remains insufficient. To address this gap, this study defined “permanent carbon” by distinguishing the low time-variability part of embodied carbon emissions in buildings. Based on samples of 114 buildings in China, statistical and regression methods were utilized to construct a prior probability distribution model for permanent carbon of concrete structures, considering differences in building types. By applying Bayesian updating methods, posterior distributions were obtained based on latest region-specific information, addressing the issue of outdated data over time. Taking the East China region as an example, we found that the regional adjustment significantly affected carbon emission estimates, with varying adjustment factors for different building types. Transportation carbon emission benchmarks were influenced by transportation modes and distances, while distribution types remained stable. This study employs the Bayesian updating method in a novel way to help establish carbon emission benchmarks, support the formulation of carbon reduction targets, and aid the low carbon design during the early stage of building projects.

This study focuses on the actual project of rural road ‘concrete to asphalt’ pavement renovation, deeply integrating the design concept of full structure regeneration and the innovation of in-situ regeneration technology for old cement pavement. Through the life cycle assessment method, an environmental impact quantification assessment model is used. Based on the analysis of the environmental benefits of traditional pavement renovation technology (using multi-hammer crushed stone and hot mix asphalt production technology), the environmental advantages of the full-structure regeneration technology scheme are outlined, including the recycling of recycled asphalt pavement materials, application of emulsified asphalt plant mixing cold regeneration technology, and implementation of resonance crushed stone on-site regeneration technology. At the same time, sensitivity analysis methods are used to deeply analyze the sensitivity of changes in parameters such as surface and base thickness, transportation distance, and environmental factors to the overall environmental impact. The research results indicate that the optimization plan can achieve a 57.97 % reduction in total energy consumption and a 71.45 % reduction in total carbon emissions. Additionally, the recycling and utilization of RAP materials significantly reduce the demand for new mineral materials and asphalt. For a 1-kilometer road surface, mineral materials save up to 82.39 % and asphalt saves up to 27.11 %. The energy consumption and carbon emissions generated during the raw material production stage in both schemes contribute the most to the environmental impact of road construction. The analysis found that the environmental benefits of the optimization plan mainly come from the recycling of the old cement concrete pavement as the base layer after resonance crushing, as well as the use of emulsified asphalt mixture cold recycling instead of the lower layer of hot mix asphalt concrete, achieving full-scale recycling of the pavement renovation. The relevant findings provide a data foundation or the improvement and upgrading of energy-saving and emission reduction technologies in road surface renovation and offer valuable insights for future research on sustainable road surface renovation strategies.

This study introduces a comprehensive framework designed to assess the carbon footprint of urban road networks, covering all stages from production to maintenance. Complementing this framework, a dedicated prediction model is also developed. Utilizing a machine learning methodology, specifically the Gradient Boosting Decision Tree (GBDT) algorithm, this model aims to accurately predict the material stock of urban road networks, thereby enhancing the overall understanding of their environmental impact. A case study focusing on Nanjing's infrastructure revealed that material production constitutes 78 % of the total emissions. Additionally, it was observed that the growth in the carbon footprint of Nanjing's urban road network has slowed down in recent years. The machine learning-based model, analyzing data from 2014 to 2020, accurately predicted the total material stock for 2021 and the stocks of different road construction materials, with a relative error under 2 %. This approach aids in the design of carbon-neutral urban transport systems and represents a stride in combating climate change.

Establishing a comprehensive and systematic method for quantifying carbon emissions (CEs) of asphalt pavement is challenging due to the multifaceted and diverse nature of the involved processes. Utilizing a life cycle assessment framework, this study introduces a practical model system for capturing CEs across the entire lifespan of asphalt pavement. A pivotal aspect of our approach involves calculating activity levels. Material quantities are ascertained based on mixture proportions and specific pavement information. We employ Ridge Regression and the quota method to approximate the energy consumption of construction machinery. A comparative analysis is performed between two CE computation models that use energy consumption and operational time as activity levels, respectively. Additional vehicle fuel consumption is translated based on shifts in the International Roughness Index, as predicted by the Mechanistic-Empirical Pavement Design Guide. CEs from traffic disruptions are calculated collaboratively through VISSIM simulations and MOVES emission modeling. Our findings reveal that material production, mixture preparation, and maintenance duration are the principal factors contributing to CEs throughout the asphalt pavement lifecycle. Notably, emission estimates for machinery in the construction phase can vary by as much as 8.3 times across different models. The study suggests that incorporating the use of recyclable materials and designing more durable pavement structures can offer effective avenues for CE mitigation.

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management.