Evidence map›Paper›PMID 42236543›Full record

ArticleScientific reports2026

A comprehensive investigation of energy consumption and carbon emissions for a new type of cold mix asphalt technology.

Qian Guo, Zhuan Wang

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Qian GuoDepartment of Architectural Engineering, Guizhou Communications Polytechnic University, Guiyang, 551400, China. guoqian_gzcpu@163.com.
Zhuan WangDepartment of Architectural Engineering, Guizhou Communications Polytechnic University, Guiyang, 551400, China.

Funding

Science and Technology Project of Guizhou Provincial Department of Transport, China Grant No. 2023-123-036
6 · The paper itself

Abstract

Cold mix asphalt (CMA) offers promising potential for reducing the energy consumption and greenhouse gas (GHG) emissions associated with traditional hot mix asphalt (HMA). This study presents a comprehensive investigation of the energy consumption and carbon emissions associated with a newly developed CMA technology (New-CMA) incorporating a hydroxylated cycloalkane-based polymer additive, which improves workability at ambient temperatures. Energy consumption and carbon emissions were quantified across the whole production stages, using energy consumption method and emission factor method recommended by the Intergovernmental Panel on Climate Change (IPCC). The results demonstrate that the New-CMA achieves a 48% reduction in total energy consumption and a 40% reduction in carbon emissions compared to conventional HMA. Significant reductions were observed particularly in the mixture production stage, where energy use and carbon emissions decreased by 97.6% and 93.6%, respectively, due to the elimination of aggregate heating. Compared to traditional CMA, the New-CMA shows reductions of 9% in energy use and 10% in carbon emissions. These improvements are primarily attributed to the elimination of aggregate heating, lower mixing temperatures, and enhanced mixing efficiency. The technology is particularly suitable for preventive maintenance and structural layers in low-to-medium traffic roads. This study provides a comprehensive environmental evaluation and offers guidance for promoting sustainable pavement practices in urban construction.

Indexed as

Environmental sustainabilityGreenhouse gasesPavement materialsSustainable infrastructure

Identifiers

PMID42236543
PMCPMC13469493

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.