Energy Management to reduce carbon emission in Pakistan

The world has seen leaps of development in the last century, the cost of which may be considered in the form of environmental degradation, increase in the level of Green house Gases (GHGs), deforestation and global warming. Carbon footprint, carbon offset carbon credits/permits, emissions trading and clean development mechanisms are some of the new terminologies that have been developed to evaluate the extent of part played by different nations in increasing global warming and the steps to mitigate it. International efforts such as Kyoto Protocol to the United Nations Framework Convention on Climate Change, UN-REDD (Reducing Emissions from Deforestation and forest Degradation) Programme, Copenhagen Accord (2009) and Cancun summit (2010) have been made to stabilize the global carbon emissions. The main hurdle in developing a global consensus is the disagreement between the developing and the developed nations with developed nations not ready to cut down on their carbon emissions.

<p indent="0mm">Industrial civilization causes huge carbon emissions, accelerates climate change, and hinders the sustainable development of human society. The construction industry causes about 40% of annual global anthropogenic carbon emissions, within which the massive construction of concrete structures alone accounts for more than 10% of the global emissions. China’s national goals for carbon peaking, neutrality, and the low-carbon development consensus call for the low-carbon design theory of concrete structures in the construction industry. Starting from the scientific issues with the concrete structure-environment symbiotic system, this paper clarifies the indicators, design methods and tuning means of low-carbon structural design, aiming to lay a foundation and provide suggestions for the development of low-carbon design, and promote the sustainable development of concrete industry. By briefly reviewing the history of the concrete industry, we illustrate the trend of low-carbon transitions, for which it is urgently needed to shift the perspective of structural design from only centering on human needs to meeting the sustainable needs of the integrated system of concrete structures and environments. Based on the characteristics of carbon emissions and uptake of concrete structures, the significance of regulating concrete structures’ net embodied carbon emissions for climate change mitigation is clarified. Further, to cope with climate change, we put forward the scientific issues and design requirements for the concrete structure-environment symbiotic system. To facilitate the sustainable development of the concrete structure-environment symbiotic system, we focus the low-carbon design method on the quantitative characterization of structure-environment dynamic coupling. The structural sustainability indicator is established to simultaneously reflect the structural reliability and carbon emission level of concrete structures. Further, we sort out relevant evaluation and design methods into the evolution of low-carbon design from the structural reliability design considering sustainability to the sustainability design characterized by a bidirectional perspective, which means the design methodology has developed from the reliability guarantee of qualitative low-carbon strategies facing climate change to quantitative carbon emission target guarantee in the form of conditional probability control, which ensures both low-carbon emissions and reliable service performance during the life cycle of concrete structures under climate change. Further, in order to assist the low-carbon design regulation, we put forward the carbon emission-based 3R^+C principles, i.e., carbon reduction, carbon reuse, and carbon recycling, to construct a low-carbon design technology system with superimposable carbon emission reduction benefits among categories. Typical low-carbon technologies for concrete structures are classified following the three principles according to their main emission reduction advantages, i.e., embodied carbon reduction in material production, embodied carbon allocation by construction mode transition, and carbon uptake with a developed end-of-life management system, and their emission reduction potential and promotion prospects are explored. A lot of future work is needed for the innovative development and promotion of low-carbon design of concrete structures, which includes clarifying the carbon emission benchmarks and goals for the concrete industry, strengthening the identification and management of failure risks and possible failure consequences under climate change, enriching and improving available low-carbon design technologies, and developing codes and software for integrated low-carbon design.

Methane emissions along biomethane and biogas supply chains are underestimated

The rapid development of the global transportation industry has led to increased carbon dioxide emissions, intensifying the pressure to reduce these emissions. On the basis of constructing a global carbon emission analysis framework for the transportation industry, this study used panel data on carbon emissions from the transportation industry in 140 countries or regions for a long-term time series from 1971 to 2018. The standard deviation ellipse, Gini coefficient, and Moran’s I index were used to characterize the spatial patterns of carbon emissions in the global transportation industry. The factors influencing carbon emissions from the global transportation industry were analyzed using quantile regression. The main findings are as follows: (1) From the distribution pattern, the total carbon emissions from the global transportation industry showed a significant upward trend, and the spatial polarization characteristics were particularly significant. (2) The Gini coefficient of global carbon emissions from the transportation industry showed a significant downward trend, characterizing a more balanced spatial distribution. (3) From the perspective of correlation patterns, the spatial distribution of carbon emissions from the global transportation industry was positively correlated. (4) Regarding influencing factors, population size had a significant role in promoting carbon emissions from the transportation industry, and the difference was not apparent. The influence of affluence on carbon emissions was basically in line with the characteristics of the Kuznets curve, technological advances had a significant negative influence on carbon emissions, and participation in the global value chain had a significant influence on carbon emissions from countries or regions with high carbon emissions. In conclusion, it is necessary to enhance international cooperation on carbon emission management in the global transportation industry and adopt differentiated policy measures. For instance, we should accelerate the construction of a multimodal transport system, increase the promotion and support for new energy heavy-duty trucks, implement policies such as priority road rights for new energy heavy-duty trucks and reduce tolls on expressways, and deepen the integration of transportation and energy.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

<p indent=0mm>Cities account for more than 70% of global carbon emissions and play an important role in mitigating climate change and achieving carbon peak and carbon neutrality. As the Paris Agreement emphasizes the need to reach global peaking of greenhouse gas emissions as soon as possible, it is significant to predict carbon emissions at the city level. However, the current COVID-19 pandemic has dramatically impacted global socioeconomic development and carbon emissions, downplaying the reference value for most urban carbon emission prediction models. In fact, existing studies on urban carbon emission prediction have also suffered from some shortcomings, such as unclear analyses of the impact of the pandemic, single scenario prediction, unified setting of growth rates, and failure to provide decision support for the government’s carbon peak work. Therefore, a multi-scenario study on urban carbon emission prediction and carbon peak in the post-pandemic period would provide local governments with scientific data to make their carbon peak action plan. To that end, we set five-carbon emission scenarios: bussiness as usual (BAU), high emissions (HE), extremely high emissions (EHE), low emissions (LE) and extremely low emissions (ELE). Based on the Monte Carlo method, we adjust the probabilities of different periods and different carbon emission scenarios to simulate uncertain evolution of carbon emissions as well as carbon emission reduction. Combining with multi-scenario analyses with the Mann-Kendall trend test and Theil Sen’s trend slope estimation method, we predict carbon emissions of the Pearl River Delta Urban Agglomeration (PRD) from 2021 to 2035 and analyze the evolution path of PRD’s carbon emissions as well as its potential for carbon peak and carbon emission reduction from 2006 to 2035. Discussions are made on the possibility of achieving conditional areas’ carbon peak goal in 2025 in Guangdong and China’s carbon peak goal in 2030. We find that: (1) Carbon emissions of PRD increased rapidly from 2006 to 2016. Dynamic simulation shows that carbon emissions a significant peak in 2020 and decrease to 248.85 M~270.06 Mt in 2035. Carbon intensity decreases by 84.18%–85.21% from 2006 to 2035. Based on the emission reduction of the BAU scenario, the cumulative carbon emission reduction potential of the LE scenario and ELE scenario is as high as 304.86 M and 587.22 Mt from 2021 to 2035. Carbon emission reduction potential based on dynamic simulation of random combination scenario is between −81.68 and 128.25 Mt, with a probability of 67.65% to achieve further emission reduction. The probability of reducing 27.44 Mt carbon emissions is the largest. (2) Shenzhen, Zhuhai, Huizhou and Dongguan are four cities that show an inverted “U” shaped evolution path to achieve carbon peak. All of them reach the carbon peak no later than 2020. From 2006 to 2035, especially after the carbon peak, carbon emissions of these cities will decrease significantly. Their carbon emissions will reduce by 14.15 M–15.40 Mt, 9.17 M–9.94 Mt, 24.07 M–26.08 Mt and 22.36 M–24.24 Mt in 2035, respectively. The cumulative carbon emission reduction potential from 2021 to 2035 is −7.99 M–8.69 Mt, −3.48 M–4.87 Mt, −5.97 M–15.39 Mt and −8.77 M–12.62 Mt, respectively. However, being earlier to reach a carbon peak reduces their carbon emission reduction potential from 2021 to 2035. (3) Guangzhou, Foshan, Zhongshan, Jiangmen and Zhaoqing are five cities that could potentially reach carbon peaks but with divergent evolution paths. Some scenarios are at risk of not reaching a carbon peak. The possibility for Guangzhou, Foshan and Zhongshan to achieve the carbon peak target of conditional areas in Guangdong Province in 2025 is more than 96.01%, while that for Jiangmen and Zhaoqing is less than 20.08%. Moreover, there is a possibility of 2.04% for Jiangmen and Zhaoqing not to reach a carbon peak. In 2035, the emission reduction of the five cities will be 56.90 M–61.87 Mt, 44.35 M–48.16 Mt, 23.92 M–25.91 Mt, 33.78 M–36.58 Mt and 20.15 M–21.88 Mt, respectively. The cumulative carbon emission reduction potential of these cities from 2021 to 2035 is significant, which is −23.75M–26.60 Mt, −17.51 M–<sc>22.17 Mt,</sc> −6.64 M–12.19 Mt, −7.57 M–17.82 Mt and −3.86 M–11.79 Mt, respectively. (4) Being earlier to reach a carbon peak is conducive for cities to reduce carbon emissions. The curve of cumulative carbon emission reduction potential shows that the marginal potential of carbon emission reduction increases with time. So early adoption of emission reduction measures and early realization of carbon peak will promote carbon emission reduction. When making action plans for carbon peak, we should prevent cities from reaching false carbon peak during the platform period, pay attention to the demonstration and acceleration effect of carbon peak cities with relatively high carbon emissions, and explore the carbon emission reduction potential of cities that have difficulties in reaching carbon peak by optimizing their energy structure and utilization efficiency.

Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985–2020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 ± 9.38 PgC. Tropical zones dominated global carbon emissions (~2/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015–2020 (1.77 ± 0.44 PgC/yr) compared to those in 1985–2000 (0.69 ± 0.21 PgC/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030.

Low-carbon production of iron and steel: Technology options, economic assessment, and policy

The impact of the COVID-19 pandemic on global trade-embodied carbon emissions

The dramatic changes in land use caused by human economic activities have a profound impact on carbon emissions and ecosystem service value （ESV）. In order to explore the evolution characteristics of carbon emissions and ESV on the spatial and temporal scales， based on the land use data of the Yellow River Basin from 2000 to 2020， this study used spatial autocorrelation and multivariate Logit regression models to study the spatial and temporal characteristics and spatial correlation of total carbon emissions and ESV in counties of the Yellow River Basin， then to explore the influencing factors of spatial correlation. The research findings were as follows： ① In the past 20 years， the total amount of land use carbon emissions in the basin has shown an overall growth trend， and the increasing counties were concentrated in energy-rich areas such as Inner Mongolia， Ningxia， and northern Shaanxi. The total amount of ESV increased first and then decreased， and the high value counties were mainly distributed on the edge of the Yellow River Basin， among which Qumalai County in Qinghai Province had the most ESV. The low value counties of ESV were mainly located in the economically active urban agglomerations such as the Shandong Peninsula Region， Central Plains Region， Guanzhong Plains Region， and cities along the yellow river in Ningxia. The lowest value of ESV has always been located in Xi'an. ② There was a spatial negative correlation between total carbon emissions and total ESV. The number of counties with high carbon emissions and high ESV has been increasing， mainly distributed in southern Inner Mongolia， eastern Ningxia， and northern Shaanxi， which was related to the location near the Yellow River and energy development. The double low type was mainly located in the gully area of the Loess Plateau， which is connected to the strip from the east and west. The low-high class was contiguously distributed in Qinghai， Sichuan， and western Gansu， and some were island-like distributed around the double-low class. The number of high-low classes was increasing year by year， mainly located in the core city area. ③ In low ESV counties， regions with better economic development and higher population were more likely to increase their carbon emissions. Taking the low carbon emissions from land use as a reference， the per capita GDP， energy use efficiency， and rainfall were significantly negatively correlated with the high-high and high-low categories. This indicates that most counties with high carbon emissions had relatively dense populations and less rainfall， resulting in higher energy dependence. Additionally， there was a positive correlation between low-high class areas and total population. When located in areas with low land use carbon emissions， areas with higher ESV values tended to have more a concentrated population distribution. The increase in land reclamation rate may encroach on forests and grasslands that can provide higher ecosystem services， reducing the value of regional ecosystem services. The research findings have certain reference significance for ecological protection and high-quality development decision-making in the Yellow River Basin.

Analysis and assessment of life-cycle carbon emissions of space frame structures

Structural decomposition analysis of global carbon emissions: The contributions of domestic and international input changes

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Will blue hydrogen lock us into fossil fuels forever?

Global carbon transfer and emissions of aluminum production and consumption