China 
Africa 
Explore chapters and articles related to this topic
The concept of sustainability
Published in Dain Bolwell, Governing Technology in the Quest for Sustainability on Earth, 2019
The Kaya identity is closely related to the I=PAT equation. But while the I = PAT equation is more general and describes a more abstract ‘impact’, the Kaya identity describes the impact of human activity specifically on carbon dioxide (CO2) emissions. It was developed by Japanese energy economist Yoichi Kaya in the book Environment, Energy, and Economy: Strategies for Sustainability arising from the 1993 Tokyo Conference on Global Environment, Energy, and Economic Development (Kaya and Yokobori 1997). The Kaya identity differs from IPAT, having four rather than three variables:global CO2emissions = global population × gross world product per global population × gross energy consumption per gross world product × global CO2emissions per gross energy consumption.
The Environmental ImPACT
Published in John C. Ayers, Sustainability, 2017
For this example, we find using dimensional analysis that the unit of I is liters of gasoline, which is equivalent to the amount of greenhouse gas emitted by auto use. We can see that the units of the product A × C is kilometers/person, a measure of the resource consumption level that can be reduced through conservation. The term T represents the inefficiency of the auto, which would be measured as gallons per mile in the United States and which could be reduced by making the car smaller and more efficient. This type of analysis can also be used to estimate the amount of carbon dioxide emitted by burning fossil fuels for energy. This application of the ImPACT identity is especially important because of its implications for global climate change, so it is named the Kaya Identity after the person who first stated it (Kaya and Yokoboi 1993, more in Section 11.2.1).
Accessibility and equity
Published in William Riggs, Disruptive Transport, 2018
How should we think about impact of new mobility systems on CO2 emissions? In 1993, Yoichi Kaya proposed a framework for thinking about carbon output as a function of human activity, GDP and global population. The framework has become known as the Kaya identity (Kaya et al. 1997). In a similar manner, carbon emissions from each mode of the transportation sector can be thought of as the product of four terms: (1) the intensity of human transportation, in passenger-kilometers, (2) the efficiency of a transportation system in combining travel of passengers into vehicles, (3) the rate of energy consumption of vehicles, and (4) the carbon intensity of the fuel source.
Integrated evaluation of energy system in Sri Lanka: a multidimensional sustainability perspective
Published in International Journal of Sustainable Energy, 2022
Konara Mudiyanselage Gayani Kaushalya Konara, Akihiro Tokai
CO2 emissions sub model was developed to calculate the emissions based on different energy sources and sectors. The relationship between the CO2 emissions and its significant drivers is based on the Kaya identity (Kaya 1989), a tool that measures the changes in CO2 emissions according to the changes of its underlying drivers, i.e. energy consumption, carbon emission, GDP and population. Kaya’s equation is as follows: whereas C = Carbon emissions (or more broadly, GHG emissions); E = Energy generated and consumed by humans; Y = Economic output (goods and services, GDP); P = Population.
A decoupling analysis of transport CO2 emissions from economic growth: Evidence from Vietnam
Published in International Journal of Sustainable Transportation, 2022
The advantage of using the Kaya identity is that each driver represents a measurable indicator influential for tracking energy and climate policy outcomes. To make the Kaya identity suitable to the transport sector we follow Timilsina and Shrestha (2009a) and include transport services. This directly links the Kaya identity to the ASIF framework (activity, modal structure, intensity, and fuel mix) providing a link to ASI (avoid, shift, improve) policy interventions (Schipper & Marie-Lilliu, 1999). Timilsina and Shrestha (2009a) write where are total transport related CO2 emissions, the emission coefficient for fuel and transport mode at time is the share of fuel consumed or fuel mix, is the energy intensity, or energy input requirements associated with mode is modal mix, is transport intensity, is income per capita and the population.13Equation (3) can be written in ratio form as where, are total transport related CO2 emissions measured in 1000 ton (kt), is the emissions coefficient where is energy consumption of fuel by mode measured in ktoe, is fuel mix, is energy intensity with is transport services measured in million ton kilometers, is mode mix, with is gross domestic product (GDP), is per capita income and is population in millions.
Regional industrial development trend under the carbon goals in China
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2023
Yuhan Xie, Yan Chen, Lifeng Wu
The rapid development of global economy has brought convenience to people’s lives, but also produced some negative effects. The emission of greenhouse gases aggravates the problem of global warming, the continuous rise in temperature will have a serious impact on the world and gradually threaten the living environment of human beings. In order to cope with climate change, China has proposed the goal of reaching the carbon peak by 2030 and achieving carbon neutrality by 2060. Since the dual carbon goals were included in the national overall development strategy, the research on carbon peaking and carbon neutrality, especially on carbon peaking, has attracted extensive attention and the research related to it has become increasingly rich. Gao, Cao, and Dan (2023) analyzed the driving factors of future carbon emissions using the Kaya identity model. Yang et al. (2023) found that industrial structure and energy structure adjustment are the main driving factors for carbon peaking. Yao, Wang, and Lei (2023) selected five characteristic factors to establish a STIRPAT model to predict carbon emissions in Shanghai. Pan and Zhang (2023) used random forest to identify the influencing factors of carbon emissions, and then predicted the carbon emissions of Gansu Province in 2030. Jiang et al. (2022) analyzed the influential elements of carbon emission efficiency with super-efficient SBM model. Yiqiong and Miao (2020) did some quantitative analysis of the influence of factors on CO2 emission efficiency. Aras and Hanifi (2022) put forward the method of Shapley addition explanatory value and explained the impact of each variable in energy consumption on carbon emission prediction. Li, Lu, and Guo (2023) used an extreme learning model to conclude that all seven regions in China can reach carbon peak before 2030 under the context of green development. Wang et al. (2022) predicted the carbon peak scenario in the North China based on the StirPat model. Chu and Zhao (2021) used the PSO-SVR method to identify factors affecting carbon emissions. Xiong et al. (2021) introduced a linear time-varying function to consider the influencing factors of carbon emissions and predict carbon emissions. Ning, Pei, and Li (2021) established an ARIMA (p, d, q) model and forecast the carbon emission trends of four representative provinces. Gao et al. (2020) came up with a gray Riccati prediction model and calculated the carbon emission reduction situation in the United States and Japan. Zhao et al. (2022) analyzed the possibility of achieving the carbon peak goal. Han and Luo (2022) explored the potential of CO2 emission reduction in three provinces under multiple scenarios. In summary, most of the above studies are about the impact of economic and social factors on carbon emissions, as well as the impact of economic or energy structure adjustments on carbon peaking.