A process-data supported input-output framework for delineating the embodied emissions of Hong Kong construction industry

Abstract

In Hong Kong, there is a lack of robust approach for calculating indirect carbon emissions. Researchers either make use of process based approach or overseas database as proxy to estimate indirect emissions; however, both approaches are constrained by data availability, data accuracy, resources limitation and truncation error. One way to solve this problem is to harness input-output based LCA to capture emissions of all economic activities between sectors. However, there is no local Input-output table (IOT), and therefore one is not able to link up monetary IOT with regional and international trade statistics, not to mention linking up the IOT model with environmental parameters to identify the indirect emissions along the supply chain. The overarching aim of this research, therefore, is to develop a methodology to systematically estimate the amount of emissions embedded in the supply chain (from cradle to site), as applied to the Hong Kong construction industry. The main objectives of the research are to (i) review and evaluate existing LCA methods for embodied carbon assessments suitable to local context; (ii) formulate top-down econometrics models including input-output table (IOT) model of Hong Kong, as well as multi-regional input-output (MRIO) model between Hong Kong and its key trading partners; (iii) establish a bottom-up approach of estimating sectoral emissions through sampled construction projects; (iv) estimate embodied emission for the whole construction sector of Hong Kong by using both input-output based process based LCA; and (v) validate the appropriateness of methodological approach, robustness of econometrics model and reliability of results. This research is unique as it offers a novel attempt that incorporates Hong Kong’s own IOT to fill the gap in traditional process based LCA. The methodology framework and estimation of embodied emission for the whole construction sector fills the knowledge gap of the literature. The two econometrics models (IOT and MRIO) have also filled the knowledge gap to enable input-output based LCA in Hong Kong. At process LCA level, this research has gathered more than a hundred of project samples, and provided useful process based data inventory under three categories of construction industry (building, civil and infrastructure, maintenance and refurbishment). The formulation of hybrid model utilize the strengths of both LCA methods (I-O and process). By harnessing disaggregation techniques and structural path analysis, the updated IOT of Hong Kong and its corresponding MRIO are broken down into emission pathways, while process data are used to supplement and enhance the I-O based LCA data. Significant statistical results of both direct, local indirect and overseas indirect emissions are derived. In future, industry practitioners can estimate direct and indirect emission base on the multipliers such as the carbon intensity, and the correlation between direct and indirect emissions to perform a quick emission assessment at project level. The future emission can also be projected on the basis of future value of construction works. Additionally, the framework would not only be used by the construction sector, a similar methodology can be replicated to develop models for more complex and diverse sectors.