“…Under the carbon peak and neutral policy, it is expected that coal emissions will remain flat or decrease in the later years, and coal carbon emissions have peaked in 2013 (Yu et al, 2018 ). This does not rule out a short-term rise in coal consumption from power plants due to the increasing electricity demand during extreme heat or cold weather (Zhang et al, 2021 , 2022 ) or economic rebound (IEA, 2022 ). Fig.…”
China is the largest CO
2
emitter in the world and announced that carbon peak and neutral targets will be achieved before 2030 and 2060, respectively. A retrospective analysis of past CO
2
emissions and their drivers is important for the actions of peaking CO
2
emissions before 2030 in China. CO
2
emissions from energy use (coal, oil, and natural gas) and cement production from 2000 to 2020 were calculated first, and their drivers were decomposed into economic and population growth, energy intensity, and emission coefficient by logarithmic mean Divisa index (LMDI) analysis in this study. China’s CO
2
emissions increased nearly threefold from 3385 in 2000 to 10,788 million tonnes (Mt) in 2020, with a decline from 2013 to 2016. Coal was the major emission sector contributing more than 70% in most years, while natural gas emissions increased nearly 13 times from 53 to 723 Mt in the two decades, although its contribution only accounted for 6.7% in 2020. Economic growth was the major positive driver, while energy intensity reduction was the major negative driver of the emission increments by year and by the Five Year Plan (FYP). Emission coefficient reduction gradually became important due to its negative effect, especially in the 13th FYP, which offset ~ 30% of the emissions induced by economic growth. The projections of CO
2
emissions in 2025, 2030, and 2035 could be 11,596 ± 582, 11,774 ± 621, and 11,401 ± 672 Mt, respectively, suggesting that China’s carbon emissions could peak around 2030 with an increment of ~ 1000 Mt on the 2020 levels. Under the sustainable growth of the economy and population, it is possible to reduce the carbon peak value or achieve peak time earlier through the additional reduction of energy intensity and emission coefficient by technological progress and energy alternatives such as non-fossil fuels.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1007/s10668-022-02811-8.
“…Under the carbon peak and neutral policy, it is expected that coal emissions will remain flat or decrease in the later years, and coal carbon emissions have peaked in 2013 (Yu et al, 2018 ). This does not rule out a short-term rise in coal consumption from power plants due to the increasing electricity demand during extreme heat or cold weather (Zhang et al, 2021 , 2022 ) or economic rebound (IEA, 2022 ). Fig.…”
China is the largest CO
2
emitter in the world and announced that carbon peak and neutral targets will be achieved before 2030 and 2060, respectively. A retrospective analysis of past CO
2
emissions and their drivers is important for the actions of peaking CO
2
emissions before 2030 in China. CO
2
emissions from energy use (coal, oil, and natural gas) and cement production from 2000 to 2020 were calculated first, and their drivers were decomposed into economic and population growth, energy intensity, and emission coefficient by logarithmic mean Divisa index (LMDI) analysis in this study. China’s CO
2
emissions increased nearly threefold from 3385 in 2000 to 10,788 million tonnes (Mt) in 2020, with a decline from 2013 to 2016. Coal was the major emission sector contributing more than 70% in most years, while natural gas emissions increased nearly 13 times from 53 to 723 Mt in the two decades, although its contribution only accounted for 6.7% in 2020. Economic growth was the major positive driver, while energy intensity reduction was the major negative driver of the emission increments by year and by the Five Year Plan (FYP). Emission coefficient reduction gradually became important due to its negative effect, especially in the 13th FYP, which offset ~ 30% of the emissions induced by economic growth. The projections of CO
2
emissions in 2025, 2030, and 2035 could be 11,596 ± 582, 11,774 ± 621, and 11,401 ± 672 Mt, respectively, suggesting that China’s carbon emissions could peak around 2030 with an increment of ~ 1000 Mt on the 2020 levels. Under the sustainable growth of the economy and population, it is possible to reduce the carbon peak value or achieve peak time earlier through the additional reduction of energy intensity and emission coefficient by technological progress and energy alternatives such as non-fossil fuels.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1007/s10668-022-02811-8.
“…The capacity factor of these power plants is below 50%, many are loss-making and liable to closure in the not too distant future. 57 Coal may indeed be the big problem, but hopefully not an insuperable one.…”
In 2006 the authors posed a question to a major oil company, and members of the wider financial community. What happens if we burn all the oil you have now?...
“…However, under the goals of peaking carbon emissions and achieving carbon neutrality, constructing a comprehensive multi-energy supply system and realizing the sustainable development and green transformation of energy have become the current crucial directions in energy development [1,2]. China has rich coal reserves, currently ranking among the world's top in terms of coal storage and annual coal production [3,4]. As coal remains a major source of energy in China, it is essential to develop intelligent coal machinery equipment, enhance safety management in coal production, and improve mining technology levels [5].…”
To address the shortcomings of existing conveyor belt deviation detection methods, such as poor fault location accuracy, a low automation level and low reliability, a method that utilizes machine vision technology to detect belt deviations in belt conveyors is proposed. This method involves preprocessing operations on captured video images, including Region of Interest (ROI) extraction, grayscale processing, and noise reduction, thereby eliminating image noise and interference. To address the edge blurring due to Gaussian filtering and threshold setting issues in Canny detection, an enhanced edge detection technique using a guided filter and the Otsu method modifies the traditional Canny operator is introduced. Subsequent application of Hough Transform and least squares fitting processes delineate the edges of the conveyor belt and its rollers during operation. Utilizing the detected edges of the conveyor belt and rollers as references, a dual-baseline positioning method is for the first time proposed to quantify the deviation degree, facilitating the identification of deviation faults. After detection with the improved Canny algorithm, clearer contour binary images with fewer noise and impurities were obtained. Experiments conducted on images from various deviation scenarios yielded an average detection accuracy of 95.4% and a detection speed of 26 frames per second (FPS). This approach not only enhances the detection speed and accuracy but also reduces the frequency of conveyor belt failures and improves the operational efficiency of belt conveyors.
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