The extension of continuous carbon emission monitoring system in China’s thermal power plants under the carbon market

Abstract

China is facing severe climate pollution, thus the CO2 emissions of thermal power plants which consume a lot of fossil energy, need to be strictly monitored. At the same time, the thermal power plants and the government will face brand new environment, where the exactly appropriate monitoring approach of CO2 emission remains ambiguous. This study aims to distinguish monitoring approaches between Continuous Emission Monitoring System (CEMS) and factor-based approach on the basis of the operation features of China’s thermal power plants, analyzing the extension of CEMS. We review the major reducing greenhouse gas initiative in China—carbon market, and different emission monitoring approaches at first. We present the prospects of extension in CEMS’s technical features by analyzing an example of two generations using coal and gas, respectively, finding that CEMS is more accurate and dynamic. This study also presents the challenges by analyzing the refinement of factor-based monitoring approach. However, In contrast to many previous studies, we consider different influence in prospect and challenge from the market itself, the application experience and equipment installation basis. We finally draw an important conclusion that the factor-based monitoring approach is more suitable for China’s thermal power plants currently, but CEMS is more promising.

Accounting CO2 Emissions of the Cement Industry: Based on an Electricity–Carbon Coupling Analysis

“…Early attempts have been made with the Long-range Energy Alternatives Planning System (LEAP) model and improved with expert judgement [28,33]. Some other effective models include the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model [34], the Back Propagation Neural Network (BPNN) [9,29], the Integrated MARKAL-EFOM System (TIMES) model for industry-level prediction [21,35], the Particle With the aim of reducing the complexity and time requirements involved in collecting information on the cement production line for carbon emission accounting, while also increasing the accuracy of carbon emission accounting in the cement industry to further support China's national ETS, a novel electricity-carbon model was proposed in this study, where carbon emissions will be predicted solely by electricity data. A case study on a cement company located in southern China was conducted, assessing its historical electricity consumption and carbon emissions record over 2016, with data collected on a daily basis.…”

“…Early attempts have been made with the Long-range Energy Alternatives Planning System (LEAP) model and improved with expert judgement [28,33]. Some other effective models include the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model [34], the Back Propagation Neural Network (BPNN) [9,29], the Integrated MARKAL-EFOM System (TIMES) model for industry-level prediction [21,35], the Particle Swarm Optimization (PSO) algorithm [30,31], the system dynamic model [37], and the Verhulst Grey forecasting (V-GM) model for country-scale prediction [36]. The mentioned studies show relatively high accuracy in their results by promoting the models.…”

“…Currently, there are two mainstream international approaches for carbon emission accounting: the Emission Factor Approach (EFA) and the Continuous Emission Monitoring System (CEMS). The EFA requires less time and lower monetary costs [9], but is associated with huge uncertainties in emission evaluation due to time lag or low spatiotemporal resolution [10]. In contrast, CEMS is more accurate with high data resolution, but is less commonly adopted due to its high costs and low flexibility [8].…”