Carbon Emission Measurement Research Articles

Carbon Accounting in the Digital Industry: The Need to Move towards Decision Making in Uncertainty.

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the following research question: how do practitioners researching, working, or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that these calls for more data obscure the fact that there will always be uncertainty, and that we must learn to govern from within it.

Measurement of carbon emissions and responsibility sharing for the industrial sector in Zhejiang, China

Carbon reduction is imperative for achieving carbon peaking and neutrality. Accordingly, it is important to determine which industrial sectors have more responsibility in this matter. Based on data from Zhejiang's input-output tables, this study applies the Input-Output method to measure and compare the carbon emissions of 42 industrial sectors in Zhejiang Province from 2002 to 2017, and then assesses the carbon emission attributes of each industrial sector, and ultimately determines the responsibility for carbon emission reduction from the perspectives of the producers and consumers. The results of the study show that direct carbon emissions and whole-process carbon emissions in Zhejiang increased continuously from 2002 to 2017, with carbon emission intensity first decreasing and then increasing. However, carbon emission intensity was much lower in 2017 than in 2002. Over time, the attributes of carbon emissions by sector changed little. Particularly, high-carbon sectors covered most of the energy supply sectors, low-carbon sectors were mostly tertiary-related, and pseudo-low-carbon sectors were mainly found in the productive services sector. In terms of carbon emission reduction responsibilities, there are large differences in emission reduction responsibilities between sectors, with the electricity and heat sectors bearing the largest responsibilities based on their production, consumption and total carbon emission reductions. The conclusions of this study can provide some data support for the further development of carbon peak and carbon neutral plans.

Greenhouse Gas Emissions in Railways: Systematic Review of Research Progress

Rail transportation plays a crucial role in reducing carbon emissions from the transportation system, making a significant contribution to environmental impact mitigation due to the efficiency of passenger and freight rail transportation. Accurate assessment of carbon emissions resulting from rail transit is essential to quantify the positive impact of this mode of transportation on overall urban transport emission reduction. Given that measuring carbon emissions throughout the lifecycle of rail transportation involves a wide array of factors, adopting a systematic framework for analyzing these aspects is crucial. This study conducts a comprehensive review of existing research related to carbon emissions in rail transportation and its mitigation. Initially, the distinct characteristics of carbon emissions associated with rail transportation are identified, along with the complexity involved in accurately measuring these emissions. Subsequently, a comparison and analysis are conducted regarding various models for measuring carbon emissions in rail transportation. Finally, the study examines some greenhouse gas emission measurement research within the railway system. Redirecting research efforts toward measuring carbon emissions in the rail transportation system is essential to help the development of robust and effective public policies. This measure will play a crucial role in emission reduction, climate change mitigation, and the promotion of more sustainable transportation. Furthermore, the identified results propose which LCA methodology offers a valuable framework improving the quality of railway transportation emissions for future generations.

Carbon emission measurement of the envelope of a university teaching building in Hefei City

Abstract In response to the challenges posed by global warming, China has formulated dual carbon goals. In this context, the construction industry, a key carbon emitter, is vital for implementing energy-saving and emission-reducing strategies. This study zeroes in on the pivotal role of building envelope structures in carbon emissions, with a focus on a university building in Hefei. Commencing with onsite measurements to gather initial data, the study leverages simulation software to conduct an in-depth analysis. Employing orthogonal experimental design and variance analysis, it meticulously assesses the carbon emissions associated with different materials used in exterior walls, roofs and windows, considering both the material production and building operation phases. The research scrutinizes the impact of these materials on carbon emissions, with a special focus on the performance of seven distinct building retrofit schemes. Key findings of the study underscore that the type and thickness of exterior wall materials substantially influence carbon emissions during the production phase. Conversely, the choice of window materials emerges as more critical in reducing emissions during the building operation phase. The implementation of the various retrofit schemes demonstrates a tangible reduction in overall building carbon emissions. Specifically, these schemes yield a yearly reduction in carbon emissions of 2.96–3.62 tons during operation and a substantial decrease of 30.36–165.97 tons in the production phase, compared to the original structure of the case study building. These insights not only offer practical and viable strategies for the construction industry’s low-carbon development but also provide theoretical underpinnings and references for future building designs and retrofits.

A carbon calculator: the development of a user-friendly greenhouse gas measuring tool for general dental practice (Part 2).

The need to reduce carbon emissions and limit global warming to 1.5 °C has spurred various sectors towards net-zero emission goals. This paper introduces a specialised carbon calculator for dental practices to compute and monitor their carbon footprints (CFPs). The carbon calculator is developed using recent carbon modelling, utilising methodologies and data from estimating the average NHS dental practice's CFP. It employs both spend-based and activity-based carbon accounting methods, simplifying carbon emission estimation. It offers dental practices a user-friendly, rudimentary, cost-free tool to determine their baseline CFP and track sustainability progress. It includes conversion factors for patient travel, procurement and waste management, enabling practices to input data and generate personalised CFP charts. It also acknowledges assumptions and uncertainties related to procurement and waste management, emphasising the availability of personalised consultancy services for more precise carbon footprinting. This carbon calculator supports environmental sustainability in dental practices as an accessible starting point. By raising awareness of their CFP, it encourages progress in 'green dentistry' and promotes environmental responsibility in oral healthcare. The calculator is freely downloadable and part of a broader 'green dentistry' initiative. Continuous carbon emission measurement and monitoring are crucial for a sustainable future, with this tool aiding dental practitioners in their environmental contributions.

Carbon emission scenario prediction for highway construction projects

In order to carry out the research on the measurement and prediction of carbon emissions from highway projects, the prediction index system is constructed by selecting seven influencing factors, namely, urbanization rate, industrial structure, energy intensity, energy structure, key low-carbon technologies, highway construction planning, and green financial support. The carbon emission coefficient method is used to measure the carbon emissions of highway projects from the perspective of energy consumption, and finally, a carbon emission scenario prediction model based on the scenario analysis method and the LSTM algorithm of the recurrent neural network is constructed to put forward a new way of predicting the carbon emissions of highway projects. Taking Hunan province as a case study object, the carbon emission trends in the next 15 years are predicted under three scenarios: low carbon, baseline, and high carbon. The results show that the highway project in Hunan province is expected to peak in 2031 under the low-carbon scenario, in 2037 under the baseline scenario, and around 2044 under the high-carbon scenario. It is also found that energy saving and emission reduction measures have a certain lag in the emission reduction effect of highway projects. Finally, based on this, suggestions and countermeasures for carbon emission reduction of highway development in Hunan province are proposed.

Data-driven analysis of carbon emissions from buildingization under the CRISP-DM framework

Abstract As a crucial pillar of the national economy, the healthy and stable development of the construction industry provides strong support to the national economy. In the current developmental context, it is particularly important to address the relationship between growth and low-carbon emission reduction. This paper proposes a study of carbon emissions from buildings using the CRISP-DM framework. According to the current situation of carbon emissions from building construction, data on the building stage is obtained from two parts: prefabricated works and on-site pouring. Because the original list data of engineering materials is partially missing, the data is processed by the weighted average method for each building material usage and carbon emission to avoid affecting the analysis results of the overall data. Combining the carbon emission measurement formula, carbon emission factor, and CRISP-DM, the carbon emission measurement model for buildingization is constructed using CRISP-DM. According to the model, the carbon emission in the transportation phase of prefabricated building materials is 1039925.978 kgCO2e, and the carbon emission of C30 concrete is the largest, with a value of 510,156.027 kgCO2e. In addition, the carbon emission in the transportation phase of cast-in-place building materials is 180.812 kgCO2e, of which the largest carbon emission is crushed stone (85.007 kgCO2e). This study can provide a reference for the construction industry in each province to set the carbon emission reduction target according to the local conditions, which is of practical significance to promote the low-carbon development of building construction.

Spatiotemporal trends and coordination of agricultural carbon efficiency in the Yangtze River Economic Belt and Yellow River Basin, China: An analysis of influencing factors and green finance integration.

As China's second-largest source of greenhouse gas emissions, agriculture is essential to achieving the goal of "carbon peak" and "carbon neutrality." Based on the measurement of agricultural carbon emissions (ACE) and agricultural carbon intensity (ACI) in 19 regions along the Yangtze River Economic Belt (YEB) and Yellow River Basin (YRB) in China from 2001 to 2020, this paper first uses the super-efficiency SBM model to measure ACE efficiency from static and dynamic perspectives. Then, the coupling coordination degree (CCD) between ACE efficiency and green finance in each region of the two basins is explored. Finally, Grey Relation Analysis (GRA) is used to obtain the influencing factors of CCD. The following conclusions are drawn: (1) The ACE in the YEB is almost twice that of the YRB. The ACE of the two basins generally experienced a trend of first growth and then declined, but the peak time was different. The ACI of the two basins showed a trend of continuous decline, and the decline rate of the YRB was faster. (2) The ACE efficiency of the two basins showed an overall upward trend, and the growth degree of different regions was vastly different. From the factor decomposition, the technological progress (TP) of the two basins significantly impacts the total factor productivity (TFP). (3) The CCD of ACE efficiency and green finance in the two basins increased from near imbalance to barely coordination level, and the CCD of the YEB increased slightly faster. The CCD of the two basins has a spatial difference of "downstream > midstream > upstream." (4) Among the influencing factors of the CCD of the two systems, the influencing degree of the factors is as follows from large to small: quality of human capital, level of economic development, government regulation, scientific and technological innovation ability.

A Whole-Segment Carbon Measurement Approach for Energy Systems Incorporating Knowledge Mapping Techniques

Abstract As the current carbon measurement method makes it difficult to reflect the time-space variability of carbon emission factors, it leads to the inability to clarify the carbon emission responsibility of grid-side losses. In this paper, starting from the internal power system, based on the power system carbon emission flow analysis theory and knowledge graph technology, a trend-tracking analytical algorithm based on the node conductivity matrix operation is proposed to solve the complex power allocation relationship between each generating unit and each node load, and between each generating unit and the line network loss. On the basis of this algorithm, the carbon flow tracking model of the whole power system is established according to the conversion relationship between the current and the carbon flow, and the analytical expressions of the carbon emission distribution among the power generation side, the load side and the line loss are obtained by combining with matrix operation, so as to measure the carbon emission measurement results of the whole power system and to provide data references for the responsibility sharing of carbon emission. Finally, simulation verification is carried out based on the actual system operation and load data for C city. The total amount of carbon emissions from electricity consumption is 98.8 million tCO2 when C city is used as the minimum spatial scale, while the total amount of carbon emissions from electricity consumption is 67.9 million tCO2, 14.0 million tCO2, 16.9 million tCO2, and the total amount of the three regions is 98.8 million tCO2, which is consistent with that calculated when C city is used as the minimum spatial scale. The calculation results are consistent in real-time. The carbon measurement method proposed in this paper can obtain higher spatial resolution carbon measurement results for electricity consumption.

Measuring Carbon Emissions from Green and Low-Carbon Full-Life-Cycle Feeding in Large-Scale Pig Production Systems: A Case Study from Shaanxi Province, China

In the pursuit of establishing a more environmentally sustainable and low-carbon hog farming system, the accurate quantification of emissions of greenhouse gas emanating from these systems, especially within the context of China, becomes imperative. Here, drawing insights from a life cycle approach, exhaustive field surveys, and context-specific analyses, we establish an emission measurement index system tailored to hog farming enterprises in China’s Shaanxi Province. Using this methodology, we probed the emission profiles and characteristics of three emblematic hog farming enterprises in the region. Our key findings are as follows: (1) The carbon dioxide emissions per kilogram of pork, factoring in feed cultivation, processing, and transportation, for Pucheng Xinliu Science and Technology, Baoji Zhengneng Farming, and Baoji Zhenghui Farming were quantified as 0.80298 kg, 1.52438 kg, and 0.81366 kg, respectively. (2) Presently, the methane emission coefficient due to enteric fermentation in large-scale hog farms in Shaanxi surpasses the default value set by the Intergovernmental Panel on Climate Change (IPCC). There appears to be a consistent underestimation of enteric methane emissions from live pigs in the province, as gauged against the IPCC metrics. Notably, the emission factor for fattening pigs averaged 2.61823 kgCH4/head/year, while that for breeding pigs stood at 2.96752 kgCH4/head/year. (3) When examining methane and nitrous oxide outputs from manure across various production stages, we observed that emissions from lactating pigs significantly outweigh those from other stages. Interestingly, nitrous oxide emissions from breeding pigs during fattening, finishing, and gestation remained nearly the same, regardless of the manure treatment method. (4) Under the management protocols followed by Pucheng and Baoji, the total carbon emissions from an individual fattening pig amounted to 328.5283 kg and 539.2060 kg, respectively, whereas for breeding pigs, these values were 539.2060 kg and 551.6733 kg, respectively. Further calculations showed that the average carbon footprint CF of large-scale pig farms in China was 3.6281 kgCO2/kg pork. In conclusion, optimizing feed cultivation and transportation logistics, promoting integrated breeding and rearing practices, refining feed formulation, and advancing manure management practices can collaboratively attenuate greenhouse gas emissions. Such synergistic approaches hold promise for steering the hog industry towards a greener, low-carbon, and sustainable trajectory.

Calculation and utility analysis of lychee life-cycle carbon emissions considering food loss and waste

Food loss and waste become the sever problem in the modern society. This paper studies the carbon emissions related to different circulation forms of lychee，which is a typical perishable fruit in China. First, we construct a kinetic model of lychee by using the Arrhenius equation. And then, we propose a carbon emission measurement with the unit effective carbon emission and the comprehensive carbon emission utility by using life cycle assessment. With this measurement, we measure the rate of loss and life cycle carbon emissions of four lychee's circulation scenarios, including: cold chain, room temperature, cold chain broken at the beginning and cold chain broken at the end.The results show that with the traditional carbon emission measurement, the carbon emissions generated by the cold chain circulation scenario are the highest in total carbon emissions under the four scenarios. But using the unit effective carbon emissions measurement, the total carbon emissions under the cold chain circulation situation is 34.84 % less than those of the room temperature circulation. Under the cold chain circulation scenario, the unit effective carbon emission and comprehensive carbon emission utility are 0.38 kgCO2/kg and 85.33 %, respectively. Improving the rate of cold chain circulation can improve the comprehensive carbon emission utility and achieve land-saving. Comparing with the cold chain circulation rate in China in today, when it increases to 95 %, the comprehensive carbon emission utility of lychee supply chain increases to 81.65 %, and the required land is reduced by 54.84 %.

Carbon-embedded energy coordination strategy in park-level integrated energy system considering time-varying carbon emission measurement

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing.

Calculation of Carbon Emissions and Study of the Emission Reduction Path of Conventional Public Transportation in Harbin City

As the northernmost megacity in China, the long winters, large population size, and unsustainable transport structure in Harbin determine that the city will produce relatively large carbon emissions. The transportation industry is one of the three greenhouse gas emission sources; therefore, the development of low-carbon transportation is imperative. This work compares commonly used carbon emission measurement methods and chooses a mileage method to classify the carbon emissions of conventional buses of different energy types used in Harbin in 2020. A multi-factor grey prediction model was constructed to predict the population size of Harbin and the number of conventional buses. After that, a scenario analysis method was used to analyze the fuel structure of buses in Harbin from three perspectives: a pessimistic scenario, a baseline scenario, and an optimistic scenario. The carbon emissions of conventional buses were calculated for Harbin from 2023 to 2030. Finally, by combining the prediction results and factors influencing carbon emission, a regular bus path to minimize carbon emissions is proposed. The outcome of this study shows that the carbon emission environment in Harbin will be improved by reducing vehicle energy consumption, optimizing energy structure, standardizing driving behavior, building intelligent transportation, giving priority to public transportation, and improving the road network structure.

Modeling construction and demolition waste quantities in Tanta City, Egypt: a synergistic approach of remote sensing, geographic information system, and hybrid fuzzy neural networks

A waste management strategy needs accurate data on the generation rates of construction and demolition waste (CDW). The objective of this study is to provide a robust methodology for predicting CDW generation in Tanta City, one of the largest and most civilized cities in Egypt, based on socioeconomic and waste generation statistics from 1965 to 2021. The main contribution of this research involves the fusion of remote sensing and geographic information systems to construct a geographical database, which is employed using machine learning for modeling and predicting the quantities of generated waste. The land use/land cover map is determined by integrating topographic maps and remotely sensed data to extract the built-up, vacant, and agricultural areas. The application of a self-organizing fuzzy neural network (SOFNN) based on an adaptive quantum particle swarm optimization algorithm and a hierarchical pruning scheme is introduced to predict the waste quantities. The performance of the proposed models is compared against that of the FNN with error backpropagation and the group method of data handling using five evaluation measures. The results of the proposed models are satisfactory, with mean absolute percentage error (MAPE), normalized root mean square error (NRMSE), determination coefficient, Kling–Gupta efficiency, and index of agreement ranging between 0.70 and 1.56%, 0.01 and 0.03, 0.99 and 1.00, 0.99, and 1.00. Compared to other models, the proposed models reduce the MAPE and NRMSE by more than 92.90% and 90.64% based on fivefold cross-validation. The research findings are beneficial for utilizing limited data in developing effective strategies for quantifying waste generation. The simulation outcomes can be applied to monitor the urban metabolism, measure carbon emissions from the generated waste, develop waste management facilities, and build a circular economy in the study area.

Study on Carbon Emission Measurement and Influencing Factors for Prefabricated Buildings at the Materialization Stage Based on LCA

To conduct a more in-depth study on carbon emissions and influencing factors during the materialization stage of prefabricated buildings, this paper focused on a residential prefabricated building in Beijing. The LCA method, combined with BIM technology, was utilized to establish a process-based “LCA-BIM” carbon emission statistical platform and to propose a carbon emission calculation method. The carbon emissions during the materialization stage were calculated. The results revealed that the production of building materials contributed the highest proportion of carbon emissions, accounting for 85.73% of the total emissions during the materialization stage. Specifically, reinforcing steel and concrete dominated the overall carbon emissions from building materials, accounting for 97.44% of the total. Through a quantitative analysis in the process of carbon emissions calculation, the main factors influencing the carbon emissions during the production stage of building materials were identified. This study adopts a combined approach of empirical analysis and a literature review, establishing six basic hypotheses for four aspects: material selection, energy consumption, material storage, and carbon emissions in the production stage of building materials. A structural equation model was used to theoretically validate the influencing factors in the production stage of prefabricated building materials. SPSS27.0 and AMOS28 software were employed for data analysis. From the perspective of the overall impact, material selection had the strongest overall impact on the production stage of building materials, followed by energy consumption, while material storage had the smallest overall impact. From the perspective of direct impacts, energy consumption had the strongest direct impact on the carbon emissions in the production stage of the building materials. The findings of this study can provide a theoretical reference for national institutions and businesses for carbon emission evaluation and decision-making.

Greenhouse gas emissions along the value chain in palm oil producing systems: A case study of Cameroon

The agricultural sector is the second most emitting sector globally, contributing about 18% of total global greenhouse gas (GHG) emissions. Multipurpose high in-demand commodities like palm oil contribute significantly to these emissions. The Roundtable on Sustainable Palm Oil (RSPO) with an objective to make the palm oil sector sustainable, developed a new tool to quantify carbon emissions from palm oil mills. The PalmGHG tool has been used in Southeast Asia to measure carbon emissions from palm oil production and for palm oil certification. However, no studies have used the tool to evaluate GHG emissions from palm oil mills in sub-Saharan Africa, which contributes about 24% of global palm oil production and is home to one of the last remaining primary forests in the world. In this study, we quantify GHG emissions along the crude palm oil (CPO) life cycle in a growing palm oil producing region in Cameroon. We use the RSPO PalmGHG accounting tool, to identify sources of CO2 emissions and quantify them in tons of CO2 equivalent. Six mills across the South West and Center regions of Cameroon were sampled. We found that the sources of carbon emissions from our sampled mills in decreasing order of magnitude are land conversion (78%), palm oil mill effluent (21%), fertilizer use (0.9%), mill fuel combustion (0.1%) and grid electricity utilization (0.04%). The average GHG emissions per ton of crude palm oil produced were exceptionally high at 22.3 tCO2e compared to other palm oil producing regions in the world such as Indonesia with only 1.6 tCO2e/ton of CPO. In addition, we found that planting oil palm on previously logged land instead of primary forest conversion could prevent field emissions by up to about 89% and by up to about 69% for replanting old oil palm stands. Intensification measures like improving palm oil mill extraction rates and improving yields would drastically reduce emissions per ton of crude palm oil produced. Although land conversion associated with deforestation remains the major source of palm oil GHG emissions, farm and mill practices such as reduction in fertilizer use, biogas capture and use of clean energy could help reduce emissions in the long run.

Carbon footprint estimation for tillage operations

As a larger use of agricultural machinery, the measurement of carbon emission is highly important. Quantification of the carbon footprint is important for the identification of more sustainable and environmentally friendly practices. Information about input energy (fuel energy) used by some tillage techniques was collected and converted into carbon. In order to use low emission tillage operations, the equivalent carbon emission factors of each tillage technique were determined. The carbon equivalents for using traditional tillage system were the highest comparing with other tillage systems (158.63 kg CE ha-1 and 55.63 kg CO2e ha-1), while No-till system which give (5.8 kg CE ha-1 and 5.45 kg CO2e ha-1). Improved conservation technology and management equipment can all help minimize the carbon footprints of farm machinery.

Research on Carbon Emission Structure and Model in Low-Carbon Rural Areas: Bibliometric Analysis

As the energy crisis and environmental problems are exacerbated, rural carbon emissions have gradually attracted increasing attention. Meanwhile, rural areas have the important function of ecological carbon sequestration, so the research field of carbon emissions has gradually expanded from urban to rural areas in recent years. To systematically sort out the research progress regarding low-carbon rural areas, a total of 583 papers published during 2013–2023 were acquired through the bibliometric analysis method from the “professional field of science of building technology” in the core database of Web of Science. Then, the research stage, author cooperation, institutional cooperation, national distribution, and keyword co-occurrence of “low-carbon rural areas” were analyzed via data visualization analysis software VOSviewer, and the current hot issues, such as carbon emission calculation list and elements, carbon emission measurement method/model, and energy saving and emission reduction paths, were further summarized and reviewed. The statistical analysis results show that: (1) in most of the current research on rural carbon emissions, geographical characteristics and rural carbon emissions have been rarely analyzed; (2) there is a lack of interdisciplinary research, e.g., less cooperation with geographic information or other disciplines; (3) the differences and characteristics of rural carbon emission factors in different economies and regions have rarely been analyzed. Based on the analysis, this research pointed out that future research can be comprehensively carried out in formulating rural carbon emission inventory, establishing a multi-scale and multi-regional statistical method and so on. This research also provides a comprehensive analysis and summary of the existing research on the structure and model of carbon emissions in low-carbon rural areas and also presents the problems that need to be paid attention to in the future research, pointing out the research direction for relevant scholars.

Carbon emission reduction: Understanding the micro‐foundations of dynamic capabilities in companies with a strategic orientation for sustainability performance

AbstractThis article aims to identify the micro‐foundations of dynamic capabilities that underpin a strategic orientation for sustainability performance in order to reduce carbon emissions. These micro‐foundations are examined in relation to two types of strategic orientation for sustainability performance: instrumental and stewardship. A qualitative research design is used to investigate four Dutch stock‐listed companies with either an instrumental or a stewardship strategic orientation. The research shows that certain micro‐foundations of dynamic capabilities achieve carbon emission reductions. Sensing, through measuring carbon emissions, supports a transparent raising of awareness on this issue. Seizing is based on a collaborative and carbon‐driven capturing of value enabled by clear financial incentives linked to reducing carbon emissions. Transforming is supported by an ambidextrous organizational structure and the active diffusion of knowledge concerning carbon emission reduction. Top management can deliver a significant impulse to achieve the desired level of influence related to carbon emission reduction. Our analysis reveals five dimensions (level of influence, level of transparency, level of responsibility, logic of value creation, and logic of embeddedness) that explain the relation between a strategic orientation for sustainability performance and the associated sustainable performance achieved in carbon emission reduction.

High frequency measurement of carbon emissions based on power big data: A case study of Chinese Qinghai province

In this paper, we build the electricity‑carbon model with techniques of frequency transformation, and statistical modeling. With macro statistical data released by Statistics Bureau of Qinghai Province and high-frequency power big data provided by State Grid Qinghai Electric Power Company, based on the electricity‑carbon model we apply the inventory method to measure the monthly carbon emissions of Qinghai Province and its prefectural-level cities, as well as its industry, construction, and other five industries. Additionally, we apply the same method to measure the emission reduction contribution of green power by using the data of proportion of “green power”. The results show that first there is a “double imbalanced” phenomenon for the distribution of Qinghai's carbon emissions, which means that the distribution of carbon emissions of Qinghai's prefectural-level cities is imbalanced and the distribution of carbon emissions of Qinghai's industries is also imbalanced. Second, the emission reduction effect of “green power” is significant. And the quantity of its reduction accounts for 54 % of Qinghai's total emission. Third, in comparison with the seven institutions' relative error rate which is about 7 % for measuring China's carbon emissions, our results are reliable.