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Digitalisation in manufacturing (or "industry 4.0") is expected to improve energy efficiency and thus reduce energy intensity in manufacturing, but studies show that it may also increase energy consumption. In this article, we investigate to what extent the degree of industry 4.0 is linked to energy consumption and energy intensity in ten Chinese manufacturing sectors between 2006 and 2019. We approximate the degree of industry 4.0 by combining data on a) patent intensity of industry 4.0-related technologies and b) industrial robot intensity. Our results indicate that there is no significant overall relationship between the degree of industry 4.0 and energy consumption or energy intensity, in contrast to some earlier studies in the Chinese context which find energy intensity reducing effects of digitalisation. We argue that industry 4.0 in China might have fewer energy related benefits than expected by politics and industry. Growth-inducing effects and outsourcing of energy-intensive manufacturing tasks, for instance, may counteract efficiency-related savings. To decarbonise manufacturing in line with China's proclaimed objective of carbon neutrality by 2060, policy makers and industry should identify specific opportunities and take seriously risks of industry 4.0. The focus should be on reducing absolute energy consumption as opposed to energy intensity, which may disguise digital rebound effects; and on integrating renewable energies, particularly in the most energy-intensive sectors (metals, chemicals, non-metallic minerals).

International audience; To assess the extent to which trees in a semi-arid silvopastoral system (SPS) can offset the greenhouse-gas (GHG) emissions of the system's livestock, this study used two process-based models (STEP-GENDEC-N2O and DynACof) to simulate 9 years of agricultural activity and resulting emissions in a SPS that has been operating in sahelian Senegal. STEP-GENDEC-N2O simulated soil N2O and CO2 fluxes, plus growth of the herbaceous layer, while DynACof focused on the tree layer. Outputs from the models included simulated time series of vegetative growth, water fluxes, and emissions. This output was validated through the use of published data, and measurements that were made at the SPS. Overall, the outputs from STEP-GENDEC-N2O agreed well with validation data for water fluxes, soil N, soil C, herbaceous biomass, and N2O emissions. Good agreement was also found between the measured fluxes of the SPS ecosystem, and the simulated values that were generated by combining STEP-GENDEC-N2O's simulations (of the herbaceous layer's heterotrophic respiration, autotrophic respiration, and gross primary productivity (GPP)) with DynACof's simulations of the tree layer's autotrophic respiration and GPP. Among the insights gained from the simulations was that in this SPS's sandy soils, nitrification was the dominant process that leads to N2O emissions. Our results show that the trees, at their current density (81 ha−1) offset 18 % to 41 % of the GHG emissions from livestock. With further development, the model set-up can be used for estimating the GHG offset at other tree densities, and will be useful for guiding future policies regarding climate-change adaptation and mitigation in the management of the Sahel's SPSs.

This study explores the potential of AI to enable circular business model innovation (CBMI) for industrial manufacturers and the corresponding AI capacities and dynamic capabilities required for their commercialization. Employing an analysis of six leading B2B firms engaged in digital servitization, we conceptualize the perceptive, predictive, and prescriptive capacities of AI, which enhance resource efficiency by automating and augmenting data-driven analysis and decision making. We further identify two innovative classes of AI-enabled CBMs – augmentation (e.g., optimization solutions) and automation (e.g., autonomous solutions) business models – and their main circular value drivers. Finally, our research reveals novel dynamic capabilities underpinning the innovation of AI-enabled business models – value discovery, value realization, and value optimization capabilities – which enable manufacturers to make economic and sustainable values come to life in collaborating with customers and ecosystem partners. This study represents an important step in our understanding of how AI can drive circularity and sustainable innovation in industrial digital servitization. Overall, our study contributes to practice and the academic literature on AI, circular business models, and digital servitization by highlighting the potential of AI to empower CBMs for industrial manufacturers and the underlying processes of this digital transformation. License fulltext: CC BY

Climate change mitigation and adaptation innovations in the forest-based sector can potentially have important environmental benefits but it is not clear what drives such innovations. In this paper we present the results of a 2021 survey on a sample of 293 Austrian and Slovenian enterprises in the sector using the theory of planned behaviour. Consistent with previous studies that applied this theory to forest management and other environmental topics, we have confirmed that attitudes, norms and perceived behavioural control significantly affect intentions to act. However, it was not possible to confirm an association between intentions and actual behaviour. To explain the intention-behaviour gap, we explored two factors: First, the respondent's perception of climate change as an opportunity for the forest-based sector to mitigate climate change or as a threat to operations and a need to do adaptation. Second, the belief that environmental problems are a bigger threat in more geographically distant locations (spatial bias). Whereas data on mitigation innovations were insufficient, for adaptation innovations our data indicate that the more strongly climate change is perceived as a threat or an opportunity, the more likely a company is to adapt. Results have implications both for innovation policies addressing the forest-based sector in Europe and for future research on climate change adaptation and mitigation behaviour.

One of the key elements in this transition is the securing of a large supply of sustainable biomass. In this study, the feedstock potential of long rotation poplar plantations (12–30 years with diameter of 15 of 30 cm) was determined and the properties of poplar biomass fuel were analyzed with the aim of using thermochemical conversion methods to produce biofuel. Our results demonstrate that Sweden has great potential for producing biofuels from long rotation poplar plantations, with a total of 1.8 million hectares (ha) consisting of arable (0.5 million ha) and forested arable land (1.3 million ha). Based on available land and biomass production potential, our results indicate that 10 million Mg DW could be produced annually. Regions in mid/southern Sweden have the largest potential (larger areas and higher biomass production. Our results further suggest that poplar biomass from these plantations has fuel characteristics similar to forest fuels from other conifer tree species, making the biomass suitable as feedstock for biofuel production based on thermochemical conversion methods. If 25% of the available land were used, 7.6 TWh methanol biofuels could be produced annually from 16 biofuel plants, using 160,000 Mg DW yr−1, primarily located in the southern part of Sweden. Two counties (Skåne and Västra Götaland) would be able to support their biofuel plants using poplar plantations as feedstock. Stable biofuel production in the other counties would depend on collaborating with neighboring counties. License full text: CC BY

Forest expansion in abandoned agricultural lands entails the fixation of atmospheric CO2 in the live biomass, the soil as well as in other ecosystem compartments, and in turn, has strong implications for C budgets and the design of actions to mitigate climate change all over the world. However, changes in C stocks in these compartments are still poorly understood. We assessed the main patterns and drivers of C accumulation over spontaneous colonization of abandoned agricultural lands, considering the above- and below-ground biomass (AGB and BGB, respectively), the woody debris (WD), the litter and the surface soil (SS; 0–6 cm depth), in an area of Central-North Spain. To attain this objective, we established a chronosequence of 30 plots (0.0625 ha) located in agricultural lands abandoned in 1956–1977, 1977–2005 and 2005–2017, in four different forest types. We found that the AGB and the SS accounted for the major proportion of the total C stock, but the BGB was also relatively important. Carbon stocks in all these major compartments increased linearly with the age of tree colonization. But C in the SS did not significantly differ among plots abandoned in the three periods considered and showed decreasing rates of change from youngest (i.e. those in plots abandoned in 2005–2017) to oldest (i.e. those in plots abandoned in 1956–1977) stands. Carbon accumulation was mainly driven by adult stem density and age of tree colonization in the AGB and the BGB, and by the total nitrogen content in the SS. Our findings indicate that, in these new forests, C accumulation in the live biomass is still ongoing and proceeds relatively fast and at an increasing rate, which points toward a clear potential for atmospheric CO2 fixation over the next few decades. This process, however, proceeds more slowly and shows signals of an apparent deceleration in the SS. The C stock in the surface soil is, in fact, largely disconnected from that of live biomass and varies depending on pre-abandonment land-use differences among the study sites. European Commission through the project CASE-CO2 (H2020-MSCA-IF-2017, DLV; 799885) Ministerio de Ciencia e Innovación - project IMFLEX (PID2021-126275OB-C22) Junta de Castilla y León - project SMART (JCYL-VA183P20) Producción Científica

Smart parking is common in contemporary cities. These smart parking lots are outfitted mostly with wireless sensor networks (WSNs), which are used to detect, monitor, and collect data on the availability status of all existing parking spaces in a given area. Sensors make up WSN, which may gather, process, and transmit informations to the sink. However, the power and communication limitations of the sensors have an effect on the performance and quality of the WSNs. The decrease in the battery and the energy of the nodes causes a decrease in the life of the nodes and also of the entire WSN network. In this article, we present a routing protocol that implements an efficient and robust algorithm allowing the creation of clusters so that the base station can receive data from the entire WSN network. This protocol adopts a reliable and efficient algorithm allowing to minimize the energy dissipation of the sensors and to increase the lifetime of the WSN. In comparison to alternative parking lot management protocols already in use, the simulation results of the proposed protocol are effective and robust in terms of power consumption, data transmission reliability, and WSN network longevity.