Coordination between scientific and technological innovation and the high-quality development of Baijiu industry: The coupling and decoupling perspective.

The Baijiu industry is a significant contributor to both the food industry and the light industry. Its high tax characteristics effectively promote the sustainable development of the regional economy. First, the evaluation index system of scientific and technological innovation (STI) and high-quality development of Baijiu industry (HQDBI) were constructed. The entropy-improved CRITIC method was used to measure the weights. Second, the coordination relationship and evolution trend of STI and HQDBI were explored using the coupling coordination model and the Tapio decoupling model. Then, the transfer law and key influencing factors were further investigated using the Markov chain and grey correlation, respectively. The main contribution is the dynamic evolution of the coupling and decoupling relationships from the perspective of multiple Baijiu provinces, and deeply depicts the coordination relationship and evolutionary trends of STI and HQDBI. The results show that: the spatial distribution of the coupling coordination degree shows high values in the east-west and low values in the north-south characteristics. In 2021, a pattern of coordinated development in Baijiu provinces has emerged along the Yangtze River basin. The decoupling state is mainly strong decoupling, but it remains poor in Shanxi. The coordination process is unstable and difficult to achieve leapfrog development. Coordination, sustainability and innovation environment have a greater impact on the coordination of subsystems.

Can new urbanization pilot policies promote green technology innovation in cities: Empirical evidence from China.

The development of urbanization has brought new challenges to the ecological environment, and the promotion of green technology innovation and development is widely recognized as an essential method to achieve cities' economic benefits and environmental protection. This paper examines whether the new urbanization pilot policies (NUP) increase green technology innovation (GTI) from both theoretical and empirical perspectives. This paper examines the impact of new urbanization on GTI by analyzing data from 285 cities in China between 2010 and 2021, using the multi-period DID model with the implementation of NUP as an exogenous policy shock. The study results indicate that NUP significantly affects GTI, and the conclusion still holds after the parallel trend test, placebo test, and other robustness tests. Heterogeneity analysis shows that the NUP significantly enhances GTI in low environmental pollution, non-resource-based, Medium-sized, and Central Region cities. The test of moderating effect shows that NUP has a "linkage effect" with the government's environmental attention, financial investment in innovation, and regional talent pooling. The findings of this paper provide empirical evidence and decision-making reference for promoting NUP and sustainable development of cities.

Does technological innovation in National Sustainable Development Agenda Innovation Demonstration Zones promote green development?-the case from Chengde City, China.

The National Sustainable Development Agenda Innovation Demonstration Zones (NSDAIDZs) aim to spearhead green development through scientific and technological innovation, showcasing sustainable development to other regions in China and offering valuable insights for countries worldwide. Taking Chengde City, which is one of the cities in the second batch of NSDAIDZs, as a case study, we examine the quantitative impact of technological innovation on green development. Additionally, it investigates the threshold effect of Research and development investments (R&D investments) on the relationship between technological innovation and green development. The results indicate that: (1) technological innovation has a positive promoting effect on green development, with a 1.01% increase in green development for every one unit increase in technological innovation; (2) The positive effect of technological innovation on green development becomes fully realized only when R&D investments and the upgrading of industrial structure surpass a specific threshold value. We contribute to the existing research on the connection between technological innovation and green development in innovation demonstration zones. It also provides empirical insights to foster a mutually beneficial relationship between R&D investments, industrial structure upgrading, and technological innovation, ultimately maximizing the promoting role of technological innovation in green development.

Crop yields fail to rise in smallholder farming systems in sub-Saharan Africa.

Drawing on a harmonized longitudinal dataset covering more than 55,000 smallholder farms in six African countries, we analyze changes in crop productivity from 2008 to 2019. Because smallholder farmers represent a significant fraction of the world's poorest people, agricultural productivity in this context matters for poverty reduction and for the broader achievement of the UN Sustainable Development Goals. Our analysis measures productivity trends for nationally representative samples of smallholder crop farmers, using detailed data on agricultural inputs and outputs which we integrate with detailed data on local weather and environmental conditions. In spite of government commitments and international efforts to strengthen African agriculture, we find no evidence that smallholder crop productivity improved over this 12-y period. Our preferred statistical specification of total factor productivity (TFP) suggests an overall decline in productivity of -3.5% per year. Various other models we test also find declining productivity in the overall sample, and none of them finds productivity growth. However, the different countries in our sample experienced varying trends, with some instances of growth in some regions. The results suggest that major challenges remain for agricultural development in sub-Saharan Africa. They complement previous analyses that relied primarily on aggregate national statistics to measure agricultural productivity, rather than detailed microdata.

Income inequality as a determinant of neonatal mortality in the Americas during 2000-2019: implications for the attainment of Sustainable Development Goal target 3.2.

BACKGROUND: The work of the WHO Commission on the Social Determinants of Health has been fundamental to provide a conceptual framework of the social determinants of health. Based on this framework, this study assesses the relationship of income inequality as a determinant of neonatal mortality in the Americas and relates it to the achievement of the Sustainable Development Goal target 3.2 (reduce neonatal mortality to at least as low as 12 deaths per 1,000 live births). The rationale is to evaluate if income inequality may be considered a social factor that influences neonatal mortality in the Americas. METHODS: Yearly data from 35 countries in the Americas during 2000-2019 was collected. Data sources include the United Nations Inter-agency Group for Child Mortality Estimation for the neonatal mortality rate (measured as neonatal deaths per 1,000 live births) and the United Nations University World Institute for Development Economics Research for the Gini index (measured in a scale from 0 to 100). This is an ecological study that employs a linear regression model that relates the neonatal mortality rate (dependent variable) to the Gini index (independent variable), while controlling for other factors that influence neonatal mortality. Coefficient estimates and their robust standard errors were obtained using panel data techniques. RESULTS: A positive relationship between income inequality and neonatal mortality is found in countries in the Americas during the period studied. In particular, the analysis suggests that a unit increase in a country's Gini index during 2000-2019 is associated with a 0.27 (95% CI [- 0.04, 0.57], P =.09) increase in the neonatal mortality rate. CONCLUSION: The analysis suggests that income inequality may be positively associated with the neonatal mortality rate in the Americas. Nonetheless, given the modest magnitude of the estimates and Gini values and trends during 2000-2019, the findings suggest a potential limited scope for redistributive policies to support reductions in neonatal mortality in the region. Thus, policies and interventions that address higher coverage and quality of services provided by national health systems and reductions in socio-economic inequalities in health are of utmost importance.

Past, present, and future of sustainable intensive care: narrative review and a large hospital system experience.

Healthcare systems are large contributors to global emissions, and intensive care units (ICUs) are a complex and resource-intensive component of these systems. Recent global movements in sustainability initiatives, led mostly by Europe and Oceania, have tried to mitigate ICUs' notable environmental impact with varying success. However, there exists a significant gap in the U.S. knowledge and published literature related to sustainability in the ICU. After a narrative review of the literature and related industry standards, we share our experience with a Green ICU initiative at a large hospital system in Texas. Our process has led to a 3-step pathway to inform similar initiatives for sustainable (green) critical care. This pathway involves (1) establishing a baseline by quantifying the status quo carbon footprint of the affected ICU as well as the cumulative footprint of all the ICUs in the healthcare system; (2) forming alliances and partnerships to target each major source of these pollutants and implement specific intervention programs that reduce the ICU-related greenhouse gas emissions and solid waste; and (3) finally to implement a systemwide Green ICU which requires the creation of multiple parallel pathways that marshal the resources at the grass-roots level to engage the ICU staff and institutionalize a mindset that recognizes and respects the impact of ICU functions on our environment. It is expected that such a systems-based multi-stakeholder approach would pave the way for improved sustainability in critical care.

Analyzing the tourism efficiency and its influencing factors of China's coastal provinces.

Tourism efficiency has become an important role in promoting tourism competitiveness and driving sustainable development. It is particularly important to identify and agnalyze the factors and mechanisms that affect efficiency. This paper firstly evaluates the tourism efficiency of 11 coastal provinces regions in China from 2010 to 2020 by using the DEA-BBC model that includes undesirable outputs. After that, it investigates the internal driving mechanism of the efficiency change through the Malmquist index and its decomposition. Finally, it analyzes the external influencing elements of tourist efficiency by the Tobit model. The results show that: (1) Although the average value of the tourism efficiency was changed from 0.727 to 0.707, it does not achieve the target. Its trend shows fluctuating from 2010-2020, which indicates that the tourism efficiency of most provincial regions is not optimal. The main factor that restricts tourism efficiency is scale efficiency. (2) By analyzing the dynamic trend, it is found that the average increase of technical efficiency is 14.0%, the average increase of technical change is 9.5%, and the average increase of MI index is 25.4%. It indicates that the overall tourism efficiency of 11 coastal provinces region in China is on the rise. (3) The spatial difference of tourism efficiency is significant, but there is no obvious spatial correlation. (4) The influencing factors of tourism efficiency are consumer demand, industrial structure, labor force and urbanization.

Analyzing green and sustainable land use in China's coal cities: Insights from industrial transformation.

Driven by the goal of achieving sustainable development and carbon neutrality. Addressing environmental pollution and remediating land damage have become critical challenges in resource-based cities and regions with low land use efficiency. As a response, this study focuses on the 23 provinces where China's coal resource-based cities are situated. Utilizing data from 2014 to 2020, this research employs the SBM-Undesirable model, which considers undesirable outputs in efficiency calculations, and the Tobit regression test. It aims to explore the spatio-temporal variations in industrial transformation within resource-based cities and its impact on the efficiency of green space utilization. Furthermore, it analyzes the characteristics and the extent of the influence of factors such as industrial structure adjustments on urban land use efficiency, maximizing the output of land as a factor of production. The results show that: (1) Over the 7-year period studied, China consistently made nationwide adjustments to land area and land use structure to meet the needs of urban development (2) The regression test results show that the industrial transformation of resource-based cities can promote the improvement of green space utilization efficiency. The positive influence coefficient is 0.064 and is significant at a 1% level. (3) Environmental regulation, government expenditure, international trade, and green cover play a positive role in promoting green land use. The study provides valuable insights for policymakers and urban planners seeking to foster sustainable development in resource-based cities.

The impact of technological innovation on the green digital economy and development strategies.

To investigate the interplay among technological innovation, industrial structure, production methodologies, economic growth, and environmental consequences within the paradigm of a green economy and to put forth strategies for sustainable development, this study scrutinizes the limitations inherent in conventional deep learning networks. Firstly, this study analyzes the limitations and optimization strategies of multi-layer perceptron (MLP) networks under the background of the green economy. Secondly, the MLP network model is optimized, and the dynamic analysis of the impact of technological innovation on the digital economy is discussed. Finally, the effectiveness of the optimization model is verified by experiments. Moreover, a sustainable development strategy based on dynamic analysis is also proposed. The experimental results reveal that, in comparison to traditional Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) models, the optimized model in this study demonstrates improved performance across various metrics. With a sample size of 500, the optimized model achieves a prediction accuracy of 97.2% for forecasting future trends, representing an average increase of 14.6%. Precision reaches 95.4%, reflecting an average enhancement of 19.2%, while sensitivity attains 84.1%, with an average improvement of 11.8%. The mean absolute error is only 1.16, exhibiting a 1.4 reduction compared to traditional models and confirming the effectiveness of the optimized model in prediction. In the examination of changes in industrial structure using 2020 data to forecast the output value of traditional and green industries in 2030, it is observed that the output value of traditional industries is anticipated to decrease, with an average decline of 11.4 billion yuan. Conversely, propelled by the development of the digital economy, the output value of green industries is expected to increase, with an average growth of 23.4 billion yuan. This shift in industrial structure aligns with the principles and trends of the green economy, further promoting sustainable development. In the study of innovative production methods, the green industry has achieved an increase in output and significantly enhanced production efficiency, showing an average growth of 2.135 million tons compared to the average in 2020. Consequently, this study highlights the dynamic impact of technological innovation on the digital economy and its crucial role within the context of a green economy. It holds certain reference significance for research on the dynamic effects of the digital economy under technological innovation.

Cultivating a sustainable future in the artificial intelligence era: A comprehensive assessment of greenhouse gas emissions and removals in agriculture.

Agriculture is a leading sector in international initiatives to mitigate climate change and promote sustainability. This article exhaustively examines the removals and emissions of greenhouse gases (GHGs) in the agriculture industry. It also investigates an extensive range of GHG sources, including rice cultivation, enteric fermentation in livestock, and synthetic fertilisers and manure management. This research reveals the complex array of obstacles that are faced in the pursuit of reducing emissions and also investigates novel approaches to tackling them. This encompasses the implementation of monitoring systems powered by artificial intelligence, which have the capacity to fundamentally transform initiatives aimed at reducing emissions. Carbon capture technologies, another area investigated in this study, exhibit potential in further reducing GHGs. Sophisticated technologies, such as precision agriculture and the integration of renewable energy sources, can concurrently mitigate emissions and augment agricultural output. Conservation agriculture and agroforestry, among other sustainable agricultural practices, have the potential to facilitate emission reduction and enhance environmental stewardship. The paper emphasises the significance of financial incentives and policy frameworks that are conducive to the adoption of sustainable technologies and practices. This exhaustive evaluation provides a strategic plan for the agriculture industry to become more environmentally conscious and sustainable. Agriculture can significantly contribute to climate change mitigation and the promotion of a sustainable future by adopting a comprehensive approach that incorporates policy changes, technological advancements, and technological innovations.

Designing a circular carbon and plastics economy for a sustainable future.

The linear production and consumption of plastics today is unsustainable. It creates large amounts of unnecessary and mismanaged waste, pollution and carbon dioxide emissions, undermining global climate targets and the Sustainable Development Goals. This Perspective provides an integrated technological, economic and legal view on how to deliver a circular carbon and plastics economy that minimizes carbon dioxide emissions. Different pathways that maximize recirculation of carbon (dioxide) between plastics waste and feedstocks are outlined, including mechanical, chemical and biological recycling, and those involving the use of biomass and carbon dioxide. Four future scenarios are described, only one of which achieves sufficient greenhouse gas savings in line with global climate targets. Such a bold system change requires 50% reduction in future plastic demand, complete phase-out of fossil-derived plastics, 95% recycling rates of retrievable plastics and use of renewable energy. It is hard to overstate the challenge of achieving this goal. We therefore present a roadmap outlining the scale and timing of the economic and legal interventions that could possibly support this. Assessing the service lifespan and recoverability of plastic products, along with considerations of sufficiency and smart design, can moreover provide design principles to guide future manufacturing, use and disposal of plastics.

Country-specific net-zero strategies of the pulp and paper industry.

The pulp and paper industry is an important contributor to global greenhouse gas emissions1,2. Country-specific strategies are essential for the industry to achieve net-zero emissions by 2050, given its vast heterogeneities across countries3,4. Here we develop a comprehensive bottom-up assessment of net greenhouse gas emissions of the domestic paper-related sectors for 30 major countries from 1961 to 2019-about 3.2% of global anthropogenic greenhouse gas emissions from the same period5-and explore mitigation strategies through 2,160 scenarios covering key factors. Our results show substantial differences across countries in terms of historical emissions evolution trends and structure. All countries can achieve net-zero emissions for their pulp and paper industry by 2050, with a single measure for most developed countries and several measures for most developing countries. Except for energy-efficiency improvement and energy-system decarbonization, tropical developing countries with abundant forest resources should give priority to sustainable forest management, whereas other developing countries should pay more attention to enhancing methane capture rate and reducing recycling. These insights are crucial for developing net-zero strategies tailored to each country and achieving net-zero emissions by 2050 for the pulp and paper industry.

Integrated global assessment of the natural forest carbon potential.

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2-5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151-363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.