Experimenting towards a low-carbon city: Policy evolution and nested structure of innovation

Cities can play a key role in the low-carbon transition, with an increasing number of cities engaging in carbon mitigation actions. The literature on urban low-carbon transition shows that low-carbon urban development is an inevitable trend of urban sustainable future; there is a great potential albeit with some limitations for cities to reduce its carbon footprints, and there are diverse pathways for cities to achieve low-carbon development. There is, however, a limited understanding in terms of the internal mechanism of urban low-carbon transition, especially in rapidly developing economies. This paper attempts to address this gap. We examine how low-carbon policies emerge and evolve, and what are the enabling mechanisms, taking Shanghai as a case study. We developed an analytical framework drawing on system innovation theory and sustainability experiments for this purpose. A total of 186 relevant policies were selected and analyzed, which is supplemented by the interviews with stakeholders in the government to gain a deeper insight into the policy contexts in Shanghai. We found that the city's low-carbon initiatives are embedded and integrated into its existing policy frameworks. A strong vertical linkage between the central and the local governments, and more importantly, a nested structure for innovative policy practices were identified, where a top-down design is met with bottom-up innovation and proactive adoption of enabling mechanism. The structure includes two layers of experiments that facilitate learning through policy experiments across scales. The uniqueness, effectiveness, applicability and limitations of these efforts are discussed. The findings provide new theoretical and empirical insights into the multilevel governance of low-carbon transition in cities

Impact of dietary manganese on intestinal barrier and inflammatory response in broilers challenged with Salmonella Typhimurium

Growing concern for public health and food safety has prompted a special interest in developing nutritional strategies for removing waterborne and foodborne pathogens, including Salmonella. Strong links between manganese (Mn) and intestinal barrier or immune function hint that dietary Mn supplementation is likely to be a promising approach to limit the loads of pathogens in broilers. Here, we provide evidence that Salmonella Typhimurium (S. Typhimurium, 4 × 108 CFUs) challenge-induced intestinal injury along with systemic Mn redistribution in broilers. Further examining of the effect of dietary Mn treatments (a basal diet plus additional 0, 40, or 100 mg Mn/kg for corresponding to Mn-deficient, control, or Mn-surfeit diet, respectively) on intestinal barrier and inflammation status of broilers infected with S. Typhimurium revealed that birds fed the control and Mn-surfeit diets exhibited improved intestinal tight junctions and microbiota composition. Even without Salmonella infection, dietary Mn deficiency alone increased intestinal permeability by impairing intestinal tight junctions. In addition, when fed the control and Mn-surfeit diets, birds showed decreased Salmonella burdens in cecal content and spleen, with a concomitant increase in inflammatory cytokine levels in spleen. Furthermore, the dietary Mn-supplementation-mediated induction of cytokine production was probably associated with the nuclear factor kappa-B (NF-κB)/hydrogen peroxide (H2O2) pathway, as judged by the enhanced manganese superoxide dismutase activity and the increased H2O2 level in mitochondria, together with the increased mRNA level of NF-κB in spleen. Ingenuity-pathway analysis indicated that acute-phase response pathways, T helper type 1 pathway, and dendritic cell maturation were significantly activated by the dietary Mn supplementation. Our data suggest that dietary Mn supplementation could enhance intestinal barrier and splenic inflammatory response to fight against Salmonella infection in broilers

University partnerships for co-designing and co-producing urban sustainability

Universities are playing an increasingly central role in advancing sustainability at the local, regional and national scale through cross-sector collaborations. Accompanying the launch of Future Earth, interest is mounting in the co-design and co-production of knowledge and solutions for advancing global sustainability, particularly in urban areas. Place-based university partnerships appear as particularly significant vehicles for enacting co-design and co-production in the context of urban sustainability. However, the nature and role of these partnerships are not well understood, in part due to the absence of systematic analyses across multiple cases. To fill this gap, the objectives of this paper were to conduct a large-scale international survey focusing on university partnerships for urban sustainability in industrialised Europe, Asia and North America to (1) determine defining features such as focus areas, geographical scales, mechanisms, actors and motivations, and (2) identify commonly encountered drivers, barriers and potential impacts. Results indicate that partnerships most typically target energy, buildings, governance and social systems, unfold at local or city-scales, and involve collaborations with local or regional government. Our analysis shows that potential outcomes of university initiatives to co-design and co-produce urban sustainability are not limited to knowledge and policy. They also encompass the creation of new technological prototypes, businesses and new socio-technical systems, in addition to transformations of the built and natural environment. Findings also suggest that individual partnerships are making strong social, environmental and sustainability impacts, with less evidence of economic contributions. Strategies are required to enhance project management and ensure that projects address contrasting priorities and time horizons in academia and local government. Implications for policy include findings that targeted funding programmes can play a key role in fostering partnerships. Measures are also required to challenge academic norms and incentive structures that, in some cases, hinder university efforts to engage in place-based initiatives to co-design and co-produce urban sustainability

Fabrication of Cubic p-n Heterojunction-Like NiO/In 2

Oxide semiconductor In2O3 has been extensively used as a gas sensing material for the detection of various toxic gases. However, the pure In2O3 sensor is always suffering from its low sensitivity. In the present study, a dramatic enhancement of sensing characteristic of cubic In2O3 was achieved by deliberately fabricating p-n heterojunction-like NiO/In2O3 composite microparticles as sensor material. The NiO-decorated In2O3 p-n heterojunction-like sensors were prepared through the hydrothermal transformation method. The as-synthesized products were characterized using SEM-EDS, XRD, and FT-IR, and their gas sensing characteristics were investigated by detecting the gas response. The experimental results showed that the response of the NiO/In2O3 sensors to 600 ppm methanal was 85.5 at 260°C, revealing a dramatic enhancement over the pure In2O3 cubes (21.1 at 260°C). Further, a selective detection of methanol with inappreciable cross-response to other gases, like formaldehyde, benzene, methylbenzene, trichloromethane, ethanol, and ammonia, was achieved. The cause for the enhanced gas response was discussed in detailed. In view of the facile method of fabrication of such composite sensors and the superior gas response performance of samples, the cubic p-n heterojunction-like NiO/In2O3 sensors present to be a promising and viable strategy for the detection of indoor air pollution

A Residential Survey on Urban Tourism Impacts in Harbin

Abstract Tourism is becoming more and more important in the global economy, and its longterm prosperity is desired by every tourism destination. Prosperity, however, cannot be achieved successfully without the involvement of those influenced by the industry, so, evaluating residents' perceptions of tourism and involving them in as many aspects of planning and policymaking as possible are important steps in creating sustainability in tourism destination development. In attempting to fill in the research gaps in social impact analysis of urban tourism development in the Chinese context, a face-to-face survey was carried out to explore residents' perceived impacts of tourism development in Harbin, a famous tourist destination in north-eastern China. The findings of this survey suggest that residents' reaction towards local tourism development varies between different interest groups. Age, income and personal connections with local tourism were found to influence residents' perceptions to some extent

Cities: build networks and share plans to emerge stronger from COVID-19

Responses to the pandemic in India’s slums, Brazil’s favelas and Africa’s marketplaces show that networks play a crucial part in making cities more resilient. Let’s enhance and empower them

A SUPERVISED SINGULAR VALUE DECOMPOSITION FOR INDEPENDENT COMPONENT ANALYSIS OF fMRI

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive tech-nique for studying the brain activity. The data acquisition process results a tempo-ral sequence of 3D brain images. Due to the high sensitivity of MR scanners, spikes are commonly observed in the data. Along with the temporal and spatial features of fMRI data, this artifact raises a challenging problem in the statistical analysis. In this paper, we introduce a supervised singular value decomposition technique as a data reduction step of independent component analysis (ICA), which is an effective tool for exploring spatio-temporal features in fMRI data. Two major advantages are discussed: first, the proposed method improves the robustness of ICA against spikes; second, the method uses the fMRI experimental designs to guide the fully data-driven ICA, yielding a more computationally efficient procedure and highly interpretable results. The advantages are demonstrated using spatio-temporal sim-ulation studies as well as a data analysis

Systematizing and upscaling urban climate change mitigation

The question of what cities can contribute to mitigation and adapting to climate change is gaining traction among researchers and policy makers alike. However, while the field is rich with case studies, methods that provide rich data across municipalities and potentially at global scale remain underdeveloped, and comparative insights remain scarce. Here we summarize contributions to the focus issue on 'Systematizing and Upscaling Urban Climate Solutions', also drawing from presentations given at an accompanying conference in 2018. We highlight four core areas for systematizing and upscaling urban climate mitigation solutions. First, with more and better (big) data and associated machine learning methods, there is increasing potential to compare types of cities and leverage collective understanding. Second, while urban climate assessments have mostly emphasized urban planning, demand-side action as related to both behavioral change and modified social practices relevant to urban space deserve more academic attention and integration across a diverse set of social sciences. Third, climate mitigation would be intangible as a single objective at the urban scale, and measures and solutions that coordinate mitigation coherently with adaptation and broader sustainable development goals require explicit conceptualization and systematization. Forth, all insights should come together to develop governance frameworks that translate scientific exercises into concrete, realistic and organized action plans on the ground, for all cities

Distracted driving behavior recognition based on improved MobileNetV2

In recent years, research on distracted driving behavior recognition has made significant progress, with an increasing number of researchers focusing on deep-learning-based algorithms. Aiming at the problems of the existing distracted driving recognition algorithm, such as its oversized model and difficulty in adapting to low computing environments, a lightweight network MobileNetV2, is chosen as the backbone network and improved to design a distracted driving behavior detection method that is both accurate and practical. The Ghost module is employed to replace point-by-point convolution to reduce the computation, the Leaky ReLU function helps mitigate the problem of dead neurons, as it prevents gradients from becoming zero for negative inputs. Finally, the channel pruning algorithm is used to further reduce the model parameters. The experiment results on the State Farm dataset show that the model’s test accuracy can reach 94.66%, and the number of parameters is only 0.23 M. The improved model has significantly fewer parameters than the baseline model, which demonstrates the effectiveness and applicability of the method