Artículos de revistas sobre el tema "Achille tazio"

This paper is to study the possibility of emission reduction targets to be achieved. With Tapio decoupling model, the decoupling relationship of 30 provinces in11th Five-Year Planis studied. Taking the carbon intensity in 2005 (reduction benchmark), the growth rate of GDP and decoupling elasticity value as indicators, all provinces are clustered into five types of regions. Then the carbon intensity of every region in 2020 is predicted. Some useful results are presented. The growth rate of GDP has no direct impact on the realization of emission reduction targets. The higher reduction benchmark does not restrict the success of emission reduction targets. The region with higher reduction benchmark is much easier to achieve in lowering carbon intensity than the region with lower one. Without powerful relevant policy, the region with lower reduction benchmark and higher decoupling elasticity value is difficult in achieving the emission reduction targets.

Through the Tapio model, this paper measures the “decoupling and coupling” relationship between carbon emissions, carbon sinks, and economic growth of marine fisheries in nine coastal provinces of China in 2009–2019, objectively evaluates the economic benefits of carbon emissions and low-carbon development potential, and then discusses the economic development models of marine fisheries in detail. The results showed that the total carbon sink and carbon emission of China’s marine fisheries are increasing. Guangdong is dominated by “double low” economic benefits and low-carbon potential, and “double high” provinces have better resource endowment conditions; China’s marine fishery economic development is still dominated by conventional types. To further promote the sustainable development of China’s marine fisheries, all provinces should define the development orientation of marine fisheries, improve the production methods of marine fisheries according to local conditions, and adjust the industrial structure of marine fisheries in a timely manner, to achieve the low-carbon fishery goal of reducing carbon emissions and growing the economy.

This paper constructs a decoupling model for four major economic regions of China, based on the Tapio decoupling index method and VAR model for carbon emissions to compare and measure the impact of decoupling between carbon emissions and economic growth in China during 1997 to 2019. The results show that the degree of decoupling between economic growth and carbon emissions varies among different economic regions, and the decoupling status is better in all regions at the beginning of the 21st century. In general, the decoupling status in the eastern and western regions is more ideal than that in the central and northeastern regions. The impulse response and variance decomposition results show that renewable energy consumption could always reduce the growth of carbon emission intensity, and its effects are most significant in the western region. The above findings help to reveal the link between economic growth, renewable energy consumption and carbon emissions in China in recent years, and how to ensure a stable economic growth in China while increasing the share of clean energy consumption in each region to achieve carbon neutrality.

Under the pressure of low-carbon development at county level in China, this paper takes Jiangsu province as an example to analyze the relationship between economic growth and carbon emissions, aiming to provide a reference for the low-carbon development in Jiangsu and other regions in China. Based on the county-level panel data from 2000 to 2017, this paper uses the Tapio elasticity model and environmental Kuznets curve model, and focuses on the differences in regional economic development and the impacts of the 2008 global economic crisis. The results show that, in general, the decoupling effect of carbon emissions in Jiangsu counties has gradually increased during the study period. Since 2011, all counties achieved the speed decoupling, with more than half of them showing strong decoupling. The environmental Kuznets curves of carbon emissions in different income groups are established, and changed before and after the 2008 global economic crisis. In 2017, only 10 of the 53 counties were on the right side of the curve, realizing the quantity decoupling between the two. Therefore, to achieve a win–win situation between carbon emission reduction and economic growth, efforts should be made from the aspects of industrial structure and energy efficiency, and measures should be taken according to local conditions.

Dematerialization is a phenomenon in which resource consumption and pollutant discharge decrease during economic development. In order to explore the optimal paths of Beijing’s dematerialization, this study combines material flow analysis method and the Tapio decoupling model to construct a city dematerialization evaluation model, and establishes a system dynamics model to simulate the comprehensive dematerialization levels and the dematerialization levels of eight materials under four scenarios. The results show that the key factors affecting the dematerialization levels of resource and discharge end were non-metals consumption and CO2 emissions respectively. During 2016–2030, Beijing would achieve weak decoupling state under four scenarios, but the degree of dematerialization would be different. For the comprehensive dematerialization level, during 2017–2024, an industrial restructuring (IR) scenario, which would strengthen R&D investment and optimize the industrial structure, would be the optimal choice. During 2025–2030, an environmental governance (EG) scenario, which means increasing the investment in pollution control, would bring about the best dematerialization level. There would be differences in the optimal dematerialization paths for eight materials. For example, economic sustainable degrowth (ESD) and EG scenarios would be the optimal paths for dematerialization of atmospheric pollutants in the period 2017–2021 and 2022–2030, respectively.

The central area is the core region of China’s economic development. Under the current goal of carbon emission reduction, the analysis of the decoupling relationship between economic growth and carbon emissions and the carbon rebound effect will help us to formulate corresponding policies, achieve a carbon peak at an early date, and ensure high-quality economic development. Based on the energy consumption data from 2000 to 2019, the carbon emission of six provinces of the central region was calculated. The Tapio decoupling model was used to learn about the decoupling index. And then, by calculating the contribution rate of technological progress to both economic growth and carbon emission intensity, the carbon saving amount and carbon rebound amount can be calculated, and the rebound effect value of carbon emission is obtained. The results show that the economy in central China presents a trend of growth. In contrast, the carbon emission of each province shows a gradient structure with a large difference, and the economic growth and carbon emission show a weak decoupling in the past five years. We further analyzed the rebound effect of carbon emissions and found that 30% of the years in the central region have a rebound effect with values of more than one. Finally, this study puts forward policy suggestions for the early realization of carbon peaks and high-quality economic development in the central region.

The shortage of water resources has become a notable bottleneck, restricting the economic development of many countries and areas around the word, especially that of North-west China. The Inner Mongolia Autonomous Region and Shaanxi Province are important energy bases and food production areas in North-west China. However, the region is suffering from perennial drought and water shortage, which has become the most significant shortcoming for energy and food production. Guiding the decoupling between regional economic development and water consumption is a critical way to achieve sustainable development. Based on the analysis of the food and energy production value and their water consumption in North-west China from 2009 to 2019, this paper uses the Tapio model to analyze the decoupling relationship between food, energy production, and water consumption, and uses the Logarithmic Mean Divisional Index (LMDI) model to analyze the driving factors affecting decoupling. The results show that most water consumption for food and energy production in North-west China is out of the ideal strong decoupling, the decoupling status is unstable, and re-coupling occurs frequently. The increase in water intensity and the change in industrial structure are the promoting factors of decoupling between production value and water consumption in food and energy in North-west China, while the increase in production value and population size are the main restraining factors. Therefore, in pursuit of strong decoupling, the government should guide the food and energy industry to move toward implementing in water saving measures through policies and promote the enthusiasm and efficiency of the labor force through financial support and other ways. Moreover, ecological protective measures are needed to be strengthened, such as water source protection, and sewage treatment.

Based on the Tapio decoupling model, this paper discusses the decoupling relationship between the economic growth and carbon emissions of the manufacturing sector in southern Jiangsu, northern Jiangsu and middle Jiangsu during the 13th Five-Year-Plan period. By using the LMDI method, the carbon emissions and influencing factors of 31 subindustries of the manufacturing sector in Jiangsu Province from 2016 to 2020 were quantitatively analyzed by region and industry. The main findings are as follows: (1) during the 13th Five-Year-Plan period, the growth rate of the energy consumption and carbon emissions of the manufacturing sectors in southern Jiangsu, northern Jiangsu and middle Jiangsu slowed down, and the industrial structure was increasingly optimized; (2) economic growth is the primary driving force behind the manufacturing carbon emissions in the three regions of Jiangsu Province, while energy intensity is the main factor that affects the carbon-emission differences among the manufacturing subsectors in the different regions; (3) improving the energy efficiency of high-emission-intensity industries, such as the ferrous metal smelting and calendering industry, chemical industry and textile industry, is the key to reducing the carbon emissions of the manufacturing sector in the different regions of Jiangsu in the future. Jiangsu Province should promote the upgrading of the manufacturing-industry structure, and it should encourage the high-energy-consumption industry to reduce its energy intensity by technological innovation to achieve the goal of emission reduction and economic growth.

The rapid expansion of urban construction land (UCL) provides a guarantee to support rapid economic development and meet the social needs of urban residents. However, urban construction land is also an important source of carbon dioxide emissions. Therefore, it is of great research value to investigate the relationship between UCL and carbon emissions in depth. Based on this, using panel data of 57 cities in the North China Plain from 2007 to 2018, the study found that there is a strong positive correlation between UCL and CO2 emissions. It can be seen that the expansion of UCL is an important source of CO2 emissions. On the basis of this research conclusion, first, this paper uses the Tapio decoupling model to analyze the decoupling relationship between UCL and carbon emissions in the North China Plain. Then, the spatial autocorrelation analysis was applied to explore the spatial correlation characteristics of the carbon emission intensity of UCL in cities in the North China Plain. Finally, using the GTWR model to analyze the influencing factors of the carbon emission intensity of UCL, the following conclusions were drawn. In 2007–2015, the decoupling relationship performed well, but it deteriorated significantly from 2015 to 2018; in addition, there was a significant positive spatial correlation of carbon emission intensity of UCL. Various influencing factors have a significant impact on the carbon emission intensity of UCL, for example, the urbanization rate, industrial structure, economic development level, and population density have a positive impact, and environmental regulations, foreign investment intensity, land use efficiency and greenery coverage have a negative impact. The research results of this paper provide a scientific basis for making decisions and optimizing pathways to achieve carbon emission reduction from UCL in the North China Plain, as well as certain reference values for other regions to achieve low-carbon development of UCL. This is significant for exploring the optimal solution of land and carbon emissions and building a harmonious human–land relationship.

In order to reduce the depletion of land natural capital and develop economy simultaneously, it is necessary to study how to achieve the strong decoupling relationship between them. However, so far such studies have been relatively limited. Thus, taking the case of Ningxia Hui Autonomous Region, China, this paper firstly analyzes the state of land natural capital utilization in 1999–2017 by using improved ecological footprint. Then, decoupling state is quantified by Tapio decoupling model. Last, major driving factors on the decoupling relationship are explored with combination of LMDI decomposition and Kaya identity equation. Results showed that: (1) Both natural capital flows and stock depletion of cultivated land decrease obviously during the transition to corn-based intensive ecological agriculture. Grassland and water are the most unsustainable development sectors among all land types with their stock depletion intensified. Forest land and construction land could basically meet the consumer demand, but the flow occupancy of construction land is the fastest-growing segment. (2) Decoupling relationship is in an alternating state between weak decoupling and strong decoupling in 1999–2017. Wherein, the cultivated land and forest land showed a preferred decoupling state, followed by grassland, while the water and construction land showed the unfavorable expansive negative decoupling and weak decoupling. (3) Decomposition results show that intensity effect is the major factor that promotes the decoupling while economic effect inhibits the decoupling, but this negative impact is weakening in the process of industrial transformation. The other three factors affect less on the decoupling. This study has a certain reference value to construct an ecological civilization in eco-fragile regions and formulate relevant policies on the increase of land natural capital efficiency.

Economic development depends on energy consumption, which is a major source of carbon emission. How to achieve economic decarbonization has become one of the key questions urgently needing to be solved on the road of carbon peak and carbon neutral development in China. Advancing total factor productivity (TFP) of carbon emission is an important way to promote economic decarbonization. For the carbon emission TFP, current research is mainly conducted from province level or an industry perspective, and studies its deference with various geographical locations, economic development levels, urbanization levels, etc., lacking the research that combines the decoupling effect to carbon emission TFP. The carbon emission TFP of Chinese cities and how to improve it remain unclear. Therefore, based on Tapio decoupling theory, this paper firstly analyzed the decoupling effect of China’s 284 cities from 2005 to 2019, and aggregated the cities into four groups according to the decoupling effect. Then, using the DEA–Malmquist index, this paper researched the carbon emission TFP and its driving factors based on the aggregation. The result shows that weak decoupling is the main decoupling status in China. As a whole, carbon emission TFP of Chinese cities does not perform well, but it shows a growth trend over time. Strong decoupling cities outperform expansive negative decoupling cities on carbon emission TFP. Technical change and pure technical efficiency change have inhibiting effect and promoting effect on carbon emission TFP, respectively, which are the main factors for the difference of carbon emission TFP between strong decoupling cities and expansive negative decoupling cities. Based on these findings, some common but differentiated recommendations are provided for improving Chinese cities’ carbon emission TFP.

Realizing the decoupling development between the economic expansion and carbon dioxide emissions of the transport sector is of great importance if the Yellow River basin is to achieve green and low-carbon development. In this paper, we adopt the Tapio decoupling index to examine the decoupling relationship within the transport sector in the Yellow River basin, and then introduce the standard deviational ellipse to dynamically analyze the spatial heterogeneity of carbon emissions and economic growth at the provincial level. Furthermore, based on the decoupling method, we expand the traditional logarithmic mean Divisia index decomposition (LMDI) model to decompose the decoupling index into eight sub-indices, and we identify the impact of each factor on the decoupling relationship. The results indicate that the carbon emissions of the transport sector in the Yellow River basin show the non-equilibrium characteristics of “upstream region < midstream region < downstream region”. The decoupling state of the transport sector shows obvious spatial differences. The less-developed regions are more likely to present non-ideal decoupling states. The growth rate of carbon emissions in Sichuan, Qinghai, and Shandong provinces is relatively fast, and the azimuth of the transport sector’s carbon emissions shows a clockwise trend. Moreover, the inhibitory effects of urbanization on decoupling in the Yellow River basin are much greater than the non-urbanization factors. In addition to the effect of urbanization, the transport structure has a major negative effect on decoupling development in the upstream and midstream regions, while energy intensity and energy structure are key to realizing a decoupled status in the downstream region. Finally, we propose some differentiated policy recommendations.

China faces a difficult choice of maintaining socioeconomic development and carbon emissions mitigation. Analyzing the decoupling relationship between economic development and carbon emissions and its driving factors from a regional perspective is the key for the Chinese government to achieve the 2030 emission reduction target. This study adopted the logarithmic mean Divisia index (LMDI) method and Tapio index, decomposed the driving forces of the decoupling, and measured the sector’s decoupling states from carbon emissions in Xinjiang province, China. The results found that: (1) Xinjiang’s carbon emissions increased from 93.34 Mt in 2000 to 468.12 Mt in 2017. Energy-intensive industries were the key body of carbon emissions in Xinjiang. (2) The economic activity effect played the decisive factor to carbon emissions increase, which account for 93.58%, 81.51%, and 58.62% in Xinjiang during 2000–2005, 2005–2010, and 2010–2017, respectively. The energy intensity effect proved the dominant influence for carbon emissions mitigation, which accounted for −22.39% of carbon emissions increase during 2000–2010. (3) Weak decoupling (WD), expansive coupling (EC), expansive negative decoupling (END) and strong negative decoupling (SND) were identified in Xinjiang during 2001 to 2017. Gross domestic product (GDP) per capita elasticity has a major inhibitory effect on the carbon emissions decoupling. Energy intensity elasticity played a major driver to the decoupling in Xinjiang. Most industries have not reached the decoupling state in Xinjiang. Fuel processing, power generation, chemicals, non-ferrous, iron and steel industries mainly shown states of END and EC. On this basis, it is suggested that local governments should adjust the industrial structure, optimize energy consumption structure, and promote energy conservation and emission reduction to tap the potential of carbon emissions mitigation in key sectors.

Human activities and land transformation are important factors in the growth of carbon emissions. In recent years, construction land for urban use in China has expanded rapidly. At the same time, carbon emissions in China are among the highest in the world. However, little is known about the relationship between the two factors. This study seeks to estimate the carbon emissions and carbon sequestrations of various types of land based on the land cover data of 137 county-level administrative regions in Shandong Province, China, from 2000 to 2020.The study estimated the carbon emissions for energy consumption using energy consumption data and night-time light images, hence, net carbon emissions. The Tapio decoupling coefficient was used to analyze the decoupling between the net carbon emissions and construction land, and where the model for the decoupling effort was constructed to explore the driving factors of decoupling. The results showed that net carbon emissions in Shandong Province continued to increase, and the areas with high carbon emissions were concentrated primarily in specific districts of the province. The relationship between net carbon emissions and construction land evolved from an expansive negative decoupling type to a strong negative decoupling type. Spatially, most areas in the province featured an expansive negative decoupling, but the areas with a strong negative decoupling have gradually increased. The intensive rate of land use and efficiencies in technological innovation have restrained carbon emissions, and they have contributed to an ideal decoupling situation. Although the intensity of carbon emission and the size of the population have restrained carbon emissions, efforts towards decoupling have faded. The degree of land use has facilitated carbon emissions, and in recent years, efforts have been made to achieve an ideal decoupling. The method of estimation of net carbon emissions devised in this research can lend itself to studies on other regions, and the conclusions provide a reference for China, going forward, to balance urbanization and carbon emissions.

Understanding the potential association between the urbanization process and regional water shortage/pollution is conducive to promoting the intensive utilization of local water resources. In this study, the water footprint model was used to estimate water utilization status in terms of both water quantity (virtual water footprint (VWF)) and water quality (grey water footprint (GWF)) in the Beijing–Tianjin–Hebei region (China) during 2004–2017. Their potential coordination relationship with the local urbanization process represented by the gross domestic product (GDP), population (POP), and built-up area (BA) was examined using the Tapio decoupling model. The results showed that from 2004 to 2017, (1) VWF in Beijing and Tianjin showed non-significant decreasing trends, with reductions of 1.08 × 109 and 1.56 × 109 m3, respectively, while that in Hebei showed a significant increasing trend, with an increase of 5.74 × 109 m3. This indicated a gradually increasing water demand in Hebei and decreasing demand in Beijing and Tianjin. In all three regions, the agricultural sector accounted for a relatively high proportion of VWF compared to other sectors. (2) GWF in Beijing, Tianjin, and Hebei all showed declining trends, with reductions of 2.19 × 1010, 2.32 × 1010, and 1.66 × 1011 m3, respectively, indicating considerable local water quality improvement. The domestic sector contributed as the main component of GWF in Beijing, while agriculture was the main contributor in Hebei. The major contributor in Tianjin transitioned from the domestic (before 2015) to the agricultural sector. (3) We found good coordination between VWF and GDP in all three regions, as their local economic development was no longer overly dependent on water consumption. However, the expansion of urban built-up area or population would bring about accelerated depletion of water resources. (4) GWF in the three provinces showed good coordination with GDP, POP, and BA in most years, implying that the development of urbanization no longer strongly caused the pollution of water resources. In sum, policymakers should focus on improving agricultural irrigation efficiency and residents’ awareness of water conservation, so as to gradually achieve sustainable water resource management in the BTH region.

Under the requirements for high-quality development, the coordinated promotion of agricultural carbon emission reduction and agricultural product supply guarantee in China is crucial to hold the bottom line of national food security as well as promote agricultural green transformation and development. Based on such situation, from the perspective of decoupling effect, driving factors and the prediction, this paper uses panel data of 30 provinces in China from 2011 to 2020, takes the carbon emission formula, the “two-stage rolling” Tapio decoupling elasticity coefficient method, the spatial Durbin model and the Grey model optimized by the Simpson formula background value to quantify the relationship between agricultural carbon emission and agricultural product supply, analyze the driving effects of agricultural carbon emission reduction and agricultural product increase, and predict the decoupling state of agricultural carbon emission and agricultural product supply between 2021 and 2025, so as to draw a scientific basis that is conducive to the coordinated promotion of agricultural carbon emission reduction and agricultural product supply guarantee in China. The result shows that: (1) The decoupling state of agricultural carbon emission and agricultural product supply shows generally “the eastern and central regions are better than the western regions” in China, and the decoupling state has improved significantly year by year. Green technology innovation (GTI), agricultural carbon emission and agricultural product supply in China have significant spatial differences and spatial auto-correlation, which shows the spatial factors cannot be ignored; (2) Green technology innovation and agricultural carbon emission in local and adjacent provinces are both in an inverted “U-shaped” relationship, meaning that high level green technology innovation is an effective way to reduce carbon emission. Though green technology innovation and agricultural product supply in local and adjacent provinces are both in a positive “U-shaped” relationship, but the minimum value of lnGTI is greater than 0, which indicates that current level of green technology has been raised to a certain level, effectively improving the output of agricultural products; (3) Compared with those in 2016–2020 in China, it is projected that in 2021–2025 the decoupling state of agricultural carbon emission and agricultural product supply will be improved significantly, and the provinces below the optimal state will leave the extremely unreasonable strong negative decoupling state, mainly show recessionary decoupling and recessionary connection. Our findings provide Chinese decision-makers with corresponding references to formulate accountable and scientific regional policies in order to achieve high-quality development of agriculture and realize “Double carbon” target in China.

Abstract Neoadjuvant chemotherapy (NAC) is the standard of care for selected patients with high-risk early-stage breast cancer with pathologic complete response (pCR) being the most prominent predictor of favorable outcomes. Here, we sought to study the predictive capacity of integrating orthogonal diagnostic measures on predicting pCR relative to standard clinicopathologic features. We developed a computational model integrating radiology and pathology images, and tumor genomics to automatically predict pCR from multimodal data. We present an interim analysis on a cohort of 957 patients with at least one available pre-NAC data modality. The baseline AUC for pCR prediction by a trained and tested logistic regression model on 857 patients using standard clinicopathologic features including receptor subtype, demographic information, and stage was 0.77. MR images were input into a convolutional neural network (CNN) and a radiomics model. The trained CNN and radiomics models using selected images of 576 patients with pre-NAC MR images achieved AUCs of 0.65 and 0.60 on 164 hold-out test cases, respectively. We trained a multiple instance learning-based weakly supervised learning (MIL-WSL) model using 537,762 extracted tiles from whole slide images (WSI) of digital histopathology scans from 522 patients. The MIL-WSL model achieved AUC of 0.63 for pCR prediction on a hold-out test set of pre-NAC biopsies from 239 patients. A feature based classifier trained on 76 cases using tumor genomic features such as mutational burden, microsatellite instability, fraction genome altered, ploidy, purity, mutation and copy number alterations in selected genes achieved an AUC of 0.72 on 83 hold-out test cases. We then combined unimodal radiology, histopathology, and genomic predictions in a deterministic manner. This multimodal combination on an independent 68-patient test set achieved an AUC of 0.84, indicating increased power to resolve pCR than any modality alone, and over clinicopathologic baseline. Together, we present approaches to train models end-to-end using tensor fusion networks and attention-gating combined with MIL. Automated multimodal methods are here shown to improve prediction over established clinical parameters alone, motivating our ongoing efforts to refine and improve the model so as to achieve higher levels of efficiency. We anticipate these interim results will be further improved through refinement of input features and increasing the number of patients included in the final validation cohort. Citation Format: Pegah Khosravi, Elizabeth J. Sutton, Justin Jee, Timothy Dalfonso, Christopher J. Fong, Doori Rose, Edaise M. Da Silva, Armaan Kohli, David Joon Ho, Mehnaj S. Ahmed, Danny Martinez, Anika Begum, Elizabeth Zakszewski, Andrew Aukerman, Yanis Tazi, Katja Pinker-Domenig, Sarah Eskreis-Winkler, Atif J. Khan, Edi Brogi, Elizabeth Morris, Sarat Chandarlapaty, George Plitas, Simon Powell, Monica Morrow, Larry Norton, Jianjiong Gao, Mark Robson, Hong Zhang, Sohrab Shah, Pedram Razavi, MSK-MIND Consortium. Prediction of neoadjuvant treatment outcomes with multimodal data integration in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1928.

Abstract Background: Adrenergic stress (AS) reduces anti-tumor response by decreasing the frequency and function of CD8+ T- cells in the tumor microenvironment (TME), resulting in an increase in those with an “exhausted” phenotype.1 Additionally, AS increases the quantity and immunosuppressive phenotype of myeloid-derived suppressor cells (MDSC) in the TME.2 The data above suggests that β-2 AR acts akin to a tumorigenic IC which can be abrogated by using P, a well-known and highly cost efficient non-selective β-blocker. Synergistic activity of anti-PD-1 with P has been reported in several murine tumor models, including the B-16 OVA mouse model.3,4 A retrospective study has shown an improvement in overall survival (OS) in patients (pts) with metastatic melanoma (MM) treated concurrently with non-selective β-blocker and immunotherapy.5 This formed the basis for our phase I study of the combination of P (at dose levels; 10 mg, 20 mg BID, and 30mg BID) and pem 200 mg every 3 weeks in pts with MM. Our published phase I results found all 3 dose levels of P to be well tolerated, and an objective response was observed in 7/9 pts.6 A decrease in perceived stress score (PSS) in pts over time was observed. Intra-tumor ratio of (CD4+T cells + CD8+T-cells)/(MDSC+ Treg) &gt;1 in the pre-treatment biopsy was predictive of treatment response. Based on the results of the phase I study, we chose P 30 mg BID as the recommended phase II dose. These results, though preliminary, strongly support our subsequent phase II clinical trial. Methods: In this prospective, single-arm, phase II, multicenter trial, pts with unresectable stage III/IV MM and measurable disease per RECIST v1.1 will be treated with P (30 mg BID) + Pem. Pts with active CNS disease, prior therapy with PD-1/PD-L1 inhibitors, or contraindications to β-blocker are excluded. The primary objective is to evaluate the overall response rate (ORR) by immune-modified RECIST v1.1. The secondary objectives are the assessment of progression free survival and OS. A Simon two-stage design will be employed, requiring a minimum of 29 pts (17 in stage 1 and 12 in stage 2) to achieve approximately 80% power to detect a 20% increase (0.35 to 0.55) in the ORR. As an exploratory analysis, we will further report a) Baseline and on-treatment PSS and b) Chronotropic effect of P after 5-minute treadmill walk as a biomarker of response; c) Post therapy changes in the TME, with a 12 week on therapy optional biopsy d) Peripheral blood changes in T cell and MDSC subsets, and cytokines/chemokines. To date, 10 pts have been accrued on the study (NCT0384836). Citation Format: Benjamin Switzer, Manu R. Pandey, Alexandra Valentine, Agnieszka Witkiewicz, Erik Knudsen, Kristopher Attwood, Joseph Tario, Pauline Funchain, Joseph J. Drabick, Hemn Mohammadpour, Marc S. Ernstoff, Igor Puzanov, Elizabeth A. Repasky, Shipra Gandhi. β-2 adrenergic receptor (AR): Another immune checkpoint (IC)" A phase II clinical trial of propranolol (P) with pembrolizumab (Pem) in patients with unresectable stage III and stage IV melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT568.

Background:Giant cell arteritis (GCA) is characterized by cranial symptoms and large-vessel lesions (LVL) in the aorta or its branches. We retrospectively analyzed the Japanese patients newly diagnosed as GCA between 2007 and 2014, and subsequently treated with glucocorticoid (GC). The imaging studies revealed that LVLs were observed in approximately half of the GCA patients, and the LVLs were significantly associated with the increased probability of poor treatment outcomes (1).Objectives:The objective of this study is to evaluate whether the distribution of LVLs of GCA was associated with poor treatment response.Methods:In a retrospective, multi-centric, nationwide registry of GCA patients treated with GCs between 2007 and 2014, 68 newly-diagnosed GCA with LVLs by imaging were detected. All investigators were members of Japan Research Committee of the Ministry of Health, Labour, and Welfare for Intractable Vasculitis (JPVAS). Poor treatment outcomes (non-achievement of clinical remission by week 24 or relapse during 104 weeks) were primarily evaluated. Cumulative rates and median time to the first event were analyzed by the Kaplan-Meier method and the log-rank test. Associated factors with the outcomes were analyzed by using the Cox proportional hazard model.Results:The mean age was 70.5 years, and 70.6% were women. Twenty-seven (39.7%) of the 68 patients were diagnosed as having GCA by both positive temporal artery biopsy and positive imaging, and 41 (60.3%) by positive imaging. Aortic lesions were detected in 72.1% (group 2, n=49) of the 68 GCA patients with LVLs. Patients without aortic lesions were categorized into two phenotypes: large-vessel GCA with subclavian lesions (group 1, n=9) and atypical large-vessel GCA without subclavian lesions (group 3, n=10). Cranial lesions were observed in 66.7%, 55.1%, and 80.0% in the group 1, 2, and 3, respectively. The initial mean dose (SD) of prednisolone was 0.74 (0.26) mg/kg/day, and 20.6 % received methotrexate for remission induction therapy. Baseline dose of GCs and mean time to achievement of low-dose GCs (prednisolone ≤ 5 mg/day) was not significantly different among the three groups.Overall, 35 (51.5%) of the 68 patients had the event of poor treatment outcomes. Eleven patients were not able to achieve clinical remission by week 24. Relapse after achievement of clinical remission was reported in total of 24 patients; 9 between week 0 and 24, 12 between week 24 and 52, 3 between week 52 and 104. The cumulative rate of events of poor treatment outcomes over the two years was 11.1% in patients with group 1, 55.3% in those with group 2, and 88.0% in those with group 3. Mean time to events was significantly different among the three groups. Multivariable analysis showed the risk of poor treatment outcomes was likely to decrease in the group 1 (hazard ratio 0.14 [95% CI 0.02-1.03], p=0.054), while it increased in the group 3 (hazard ratio 2.22 [95% CI 1.06-4.68], p=0.035).Conclusion:The distribution of LVLs were associated with poorer treatment outcomes. A half of the patients with aortic lesions had poor treatment outcomes while subclavian arteritis without aortic lesions had better clinical outcomes. Atypical large vessel-GCA without the aortic and subclavian artery involvement was the worst prognostic phenotype of LV-GCA. Extent of LVLs by imaging should be considered when determining the treatment strategy for GCA.References:[1]Sugihara T, et al. Arthritis Res Ther. 2020;22(1):72Acknowledgements:The authors would like to acknowledge Mitsuaki Isobe (Sakakibara Heart Institute), Yoshihiro Arimura (Kichijoji Asahi Hospital), and all the investigators in the Japan Research Committee of the Ministry of Health, Labour, and Welfare for Intractable Vasculitis (JPVAS). In addition to the authors, the following investigators and institutions participated in this study: Department of Internal Medicine, Juntendo University Koshigaya Hospital (Shigeto Kobayashi); Niigata Rheumatic Center (Satoshi Ito); Niigata Prefectural Shibata Hospital (Noriyuki Homma).Disclosure of Interests:takahiko sugihara Speakers bureau: TS has received honoraria from Abbvie Japan Co., Ltd., AsahiKASEI Co., Ltd., Astellas Pharma Inc., Ayumi Pharmaceutical, Bristol Myers Squibb K.K., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Mitsubishi-Tanabe Pharma Co., Ono Pharmaceutical, Pfizer Japan Inc., Takeda Pharmaceutical Co. Ltd., and UCB Japan Co. Ltd., Grant/research support from: TS has received research grants from AsahiKASEI Co., Ltd., Daiichi Sankyo., and Ono Pharmaceutical., Haruhito A. Uchida Grant/research support from: HAU belongs to the Department of Chronic KidneyDisease and Cardiovascular Disease which is endowed by Chugai Pharmaceutical, MSD, Boehringer Ingelheim, and Kawanishi Holdings., Hajime Yoshifuji Speakers bureau: HY has received lecture fees from Chugai Pharmaceutical Co., Ltd. and Nihon Medi-Physics Co., Ltd., Yasuhiro Maejima Speakers bureau: YM have received honoraria from Chugai Pharmaceutical Co., Ltd.., Taio Naniwa Speakers bureau: TN has received lecture fees from Chugai Pharmaceutical Co., Ltd.., Grant/research support from: TN has received research grants from Chugai Pharmaceutical Co., Ltd.., Yasuhiro Katsumata Speakers bureau: YK has received honoraria from Chugai Pharmaceutical Co., Ltd., Glaxo-Smithkline K.K., Sanofi K.K., Pfizer Japan Inc., and Asahi Kasei Pharma Corp., Takahiro Okazaki Grant/research support from: TO has received research grants from Chugai Pharmaceutical Co., Ltd., Eisai Pharmaceutical., and Actelion, Jun Ishizaki: None declared, Yohko Murakawa Speakers bureau: YM has received honoraria from Abbvie, Astellas, Ayumi Pharmaceutical, Bristol Myers Squibb, Chugai Pharmaceutical, Eisai Pharmaceutical, Janssen Pharmaceutical, Kissei Pharmaceutical, Nippon Kayaku, Pfizer Pharmaceutical, Takeda Pharmaceutical, UCB Pharmaceutical, Grant/research support from: YM has received research grant support from Asahi Kasei Pharma, AbbVie Japan, Chugai Pharmaceutical, Daiichi Sankyo, Eisai Pharmaceutical, Mitsubishi Tanabe Pharma, Nippon Kayaku, Gilead Sciences Inc, Janssen Pharmaceutical, and Teijin Pharma., Noriyoshi Ogawa: None declared, Hiroaki Dobashi: None declared, Tetsuya Horita: None declared, Yoshiya Tanaka Speakers bureau: YT has received consulting fees, speaking fees, and/or honoraria from Daiichi-Sankyo, Astellas, Pfizer, Mitsubishi-Tanabe, Bristol-Myers, Chugai, YL Biologics, Eli Lilly, Sanofi, Janssen, UCB, Grant/research support from: YT has received research grants from Mitsubishi-Tanabe, Takeda, Bristol-Myers, Chugai, Astellas, Abbvie, MSD, Daiichi-Sankyo, Pfizer, Kyowa- Kirin, Eisai, Ono., Shunsuke Furuta: None declared, Tsutomu Takeuchi Speakers bureau: TT has served on speakers’ fees for AbbVie, Bristol-Myers Squibb, Chugai, Mitsubishi Tanabe, Pfizer, Astellas, Daiichi Sankyo, Eisai, Sanofi, Teijin, Takeda, and Novartis., Consultant of: TT has received consulting fees from Astra Zeneca, Eli Lilly, Novartis, Mitsubishi Tanabe, AbbVie, Nippon Kayaku, Janssen, Astellas, Taiho, Chugai, Taisho Toyama, GlaxoSmithKline, and UCB., Grant/research support from: TT has received research grants from Astellas, Chugai, Daiichi Sankyo, Takeda, AbbVie, Asahi Kasei, Mitsubishi Tanabe, Pfizer, Eisai, AYUMI, Nippon Kayaku, and Novartis., Yoshinori Komagata Speakers bureau: YK has received speakers’ fees from Bristol-Myers Squibb, Eli Lilly, Janssen, Novartis, Daiichi Sankyo, AbbVie, Nippon Shinyaku, Towa., Consultant of: YK has received consulting fees from Chugai, Kyowa Hakko Kirin, Asahi Kasei, UCB, Yoshikazu Nakaoka Speakers bureau: YN has received lecture fees from Astellas, Takeda, Daiichi Sankyo, Actelion, and Japan Blood Products Organization (JB)., Consultant of: YN has received consulting fees and/or lecture fees from AbbVie and Chugai, Grant/research support from: YN has received research grants from Chugai and Bayer Yakuhin, Ltd, masayoshi harigai Speakers bureau: MH has received speaker’s fee from AbbVie Japan GK, Ayumi Pharmaceutical Co., Boehringer Ingelheim Japan, Inc.,Bristol Myers Squibb Co., Ltd., Chugai Pharmaceutical Co., Ltd., Eisai Co., Ltd., Eli Lilly Japan K.K., GlaxoSmithKline K.K., Kissei Pharmaceutical Co., Ltd., Pfizer Japan Inc., Takeda Pharmaceutical Co., Ltd., and Teijin Pharma Ltd., Consultant of: MH is a consultant for AbbVie, Boehringer-ingelheim, Bristol Myers Squibb Co., Kissei Pharmaceutical Co.,Ltd. and Teijin Pharma., Grant/research support from: MH has received research grants from AbbVie Japan GK, Asahi Kasei Corp., Astellas Pharma Inc., Ayumi Pharmaceutical Co., Bristol Myers Squibb Co., Ltd., Chugai Pharmaceutical Co., Daiichi-Sankyo, Inc.,Eisai Co., Ltd., Kissei Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Co., Nippon Kayaku Co., Ltd., Sekiui Medical, Shionogi & Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., and Teijin Pharma Ltd.

China has proposed ambitious goals of carbon peak and carbon neutrality, and will pay more attention to the relationship between economic development and carbon emissions. It's significant to assess the current environmental decoupling and prospect the future conditions in China. This article studies the decoupling status and influencing factors in Chinese provinces from 1996 to 2018 through Tapio decoupling index and decomposition model. The results show that most provinces are currently in a weak decoupling state. The growth of per capita GDP and population will affect the process of strong decoupling, while the optimization of energy intensity, energy structure and industrial structure will promote the realization of strong decoupling. Moreover, this paper prospects the decoupling scenarios of Chinese provinces from 2019 to 2035 and finds that all provinces will achieve strong decoupling before 2040, 2035 and 2030 under different carbon emissions scenarios respectively. To achieve the strong decoupling of economic output and carbon emissions as soon as possible, the government must improve energy efficiency, use renewable energy in large quantities, and promote the industrial transformation and upgrading.

The emissions trading policy is considered a key measure for China to achieve its “double carbon” goal. In this study, two types of Tapio carbon decoupling models are constructed, panel data for 30 provinces and cities in China from 2004 to 2017 are selected, and the difference-in-differences (DID) model is used to evaluate the role of carbon trading policies in carbon decoupling. The study shows that carbon emissions trading policies can significantly promote carbon decoupling in China and that the formulation and implementation of such environmental regulations promote carbon decoupling with the help of two intermediary variables: gray technology innovation and clean technology innovation. Based on the findings, it is concluded that China should actively build a unified national carbon trading market, ensure the synergistic coupling of emission reduction targets and economic growth targets, and effectively play the role of the carbon trading market in inducing and promoting low-carbon technology innovation to help decouple carbon.

The Chinese government focuses on the high-end equipment manufacturing industry to achieve a target of carbon neutrality. This study takes China’s Bohai Rim as a case study. First, the Tapio decoupling model was used to analyze the carbon emission status of the high-end equipment manufacturing industry in the Bohai Rim. Second, LMDI was used to determine the main factors of carbon emission. Similarly, the Monte Carlo simulation predicted the time of carbon neutrality. The results found that the relationship between carbon emission and the development of the high-end equipment manufacturing industry is that of strong decoupling, but there is still a risk of “recoupling.” The scale effect is the primary driving force for carbon emission reduction in the equipment manufacturing industry, followed by a structural effect and a carbon emission intensity effect. In the baseline scenario, low-carbon scenario, and technological breakthrough scenario, carbon neutrality will be achieved before 2060. The results of the study suggest that China should improve energy utilization efficiency and encourage green innovation.

Promoting carbon emissions of the transportation industry to reach the peak as soon as possible is an effective way to control carbon emissions in China. Combined with the logarithmic mean Divisia index (LMDI) model and the Tapio decoupling model, this paper tries to predict and analyze the time, path, and quality of carbon peaking in the transportation industry in China and its eastern, central, and western regions. The research shows the following: 1) Under the “benchmark scenario,” the transportation industry in China and its three regions will not achieve the international commitment of achieving peak carbon emissions by 2030. 2) Under the “radical scenario,” the peak time of China’s transportation industry can be reached in 2027, which is 5 years earlier, 10% peak value lower than that in the “benchmark scenario.” In that scenario, in transportation industry, the eastern region will reach the carbon peak in 2025 and the central and western regions will both reach the carbon peak in 2028. By then, the peak quality of carbon emissions will be better than that under the “benchmark scenario” in transportation industry. This paper can provide theoretical support for promoting carbon emission reductions and carbon peaking in China’s transportation industry.

AbstractThe study analysed the relationship between CO2 emissions and economic growth in Ghana, specifically by analysing Ghana's decoupling status from 1990 to 2018. The Tapio elasticity method and the logarithmic mean Divisia index decomposition technique were used in the study to find out what causes CO2 emissions in Ghana to change over time. The analysis revealed that CO2 emissions and economic growth have increased over the study period, with economic growth driven mostly by the services and industrial sectors in the last decade. The decoupling index analysis shows that weak decoupling status dominated the period 1990–2018, interspersed with strong decoupling and expansive negative decoupling status. Economic structure and energy intensity, instead, were found to promote the decoupling of CO2 emissions and economic growth. From the decomposition analysis, CO2 emissions in Ghana are driven on the average by economic activities, emission factors, and population growth. To achieve the Sustainable Development Goal 13, the study suggests that policies to cut CO2 emissions should focus on economic activities, factors that affect emissions, and population growth. Also, to decouple CO2 emissions from economic growth, the implementation of policies that change the structure of the economy and energy intensity towards renewable sources should be intensified in Ghana.

As the two largest developing countries globally, China and India have become the top 1 and 3 carbon emitters, respectively. Quantitating their CO2 emissions in terms of the characteristics and driving factors is highly significant to mitigating global climate change. This study compiled the CO2 emission inventories from 1990 to 2017 in China and India. The Tapio model and index decomposition analysis were used to analyze the impact of socio-economic factors on CO2 emissions. We found that 1) CO2 emissions of China and India reached 9526 and 2242 Mt, respectively, in 2017. CO2 emissions increased during 1990–2017 with an average annual growth rate of 5% in both countries. 2) In China, the economic development has remained weakly decoupling from emissions since 2012, reaching a strong decoupling (-0.2) in 2017. In contrast, the contribution of India’s economy to emissions continued to increase, and the decoupling status showed continuous fluctuations. 3) Economic development and population explosion were the dominant factors driving CO2 emissions in the countries. The effect of energy intensity inhibited India’s emissions growth after 2008 with an impact degree lower than China. Overall, our findings on the impact of the economy and emission development may provide references for other developing countries at different stages to achieve low-carbon development.

High-energy consumption and high-emission industries contribute a lot to economic development, but their carbon emissions are also huge. In order to achieve the dual-carbon target as early as possible, it is necessary to reduce the carbon emissions of high-energy consumption and high-emission industries. This paper selected five representative factors (population, per capita gross domestic product (GDP), energy intensity, energy structure and carbon emission coefficient) and adopted the logarithmic mean divisia index (LMDI) method to decompose the driving factors of carbon emissions. Therefore, this paper uses Tapio decoupling model to analyze the decoupling relationship between the two factors with the greatest impact on carbon emissions and carbon emissions. The results show that: (i) There is a good decoupling between high-energy consumption and high-emission industries and per capita GDP, and the impact of per capita GDP on carbon emissions will gradually decrease in the future; (ii) The decoupling relationship between carbon emissions and energy intensity is poor. For some industries, the reduction of energy intensity can help reduce carbon emissions. Finally, this paper puts forward some suggestions to promote carbon emission reduction. This paper provides theoretical support for studying how to reduce carbon emissions and formulate relevant emission reduction policies in the high-energy consumption and high-emission industries.

In the context of global countries’ pursuit of sustainable development and “dual carbon” goals of China, this paper combines the Tapio decoupling model, Kaya’s equation and LMDI decomposition method to analyze the relationship between carbon emissions and economic growth and the driving factors of carbon emissions in the Yellow River Basin (YRB) of China from 2001 to 2019. It is found that the decoupling state of CO2 and economic growth in the Yellow River Basin shows a trend of expansion negative decoupling - expansion connection - weak decoupling - strong decoupling, but different regions shows some heterogeneity. The economic intensity effect and population size effect generally play a positive role in driving carbon emissions, while carbon emission intensity, industrial structure and energy structure effect play a negative role in driving carbon emissions in most years. From the regional perspective, the energy consumption structure in the middle reaches is the most important factor to promote carbon emissions. In terms of time period, the industrial structure effect is the driving factor of carbon emissions in the decoupling state overall as expansionary linkage, while it is the inhibitor of carbon emissions in the decoupling state overall as weak decoupling. This study also puts forward countermeasures such as optimize the energy structure and upgrade the industrial structure to achieve green and high-quality economic development in the YRB.