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Digitalisation in industry – also called “Industry 4.0” – is seen by numerous actors as an opportunity to reduce the environmental impact of the industrial sector. The scientific assessments of the effects of digitalisation in industry on environmental sustainability, however, are ambivalent. This cumulative dissertation uses three empirical studies to examine the expected and observed effects of digitalisation in industry on environmental sustainability. The aim of this dissertation is to identify opportunities and risks of digitalisation at different system levels and to derive options for action in politics and industry for a more sustainable design of digitalisation in industry. I use an interdisciplinary, socio-technical approach and look at selected countries of the Global South (Study 1) and the example of China (all studies). In the first study (section 2, joint work with Marcel Matthess), I use qualitative content analysis to examine digital and industrial policies from seven different countries in Africa and Asia for expectations regarding the impact of digitalisation on sustainability and compare these with the potentials of digitalisation for sustainability in the respective country contexts. The analysis reveals that the documents express a wide range of vague expectations that relate more to positive indirect impacts of information and communication technology (ICT) use, such as improved energy efficiency and resource management, and less to negative direct impacts of ICT, such as electricity consumption through ICT. In the second study (section 3, joint work with Marcel Matthess, Grischa Beier and Bing Xue), I conduct and analyse interviews with 18 industry representatives of the electronics industry from Europe, Japan and China on digitalisation measures in supply chains using qualitative content analysis. I find that while there are positive expectations regarding the effects of digital technologies on supply chain sustainability, their actual use and observable effects are still limited. Interview partners can only provide few examples from their own companies which show that sustainability goals have already been pursued through digitalisation of the supply chain or where sustainability effects, such as resource savings, have been demonstrably achieved. In the third study (section 4, joint work with Peter Neuhäusler, Melissa Dachrodt and Marcel Matthess), I conduct an econometric panel data analysis. I examine the relationship between the degree of Industry 4.0, energy consumption and energy intensity in ten manufacturing sectors in China between 2006 and 2019. The results suggest that overall, there is no significant relationship between the degree of Industry 4.0 and energy consumption or energy intensity in manufacturing sectors in China. However, differences can be found in subgroups of sectors. I find a negative correlation of Industry 4.0 and energy intensity in highly digitalised sectors, indicating an efficiency-enhancing effect of Industry 4.0 in these sectors. On the other hand, there is a positive correlation of Industry 4.0 and energy consumption for sectors with low energy consumption, which could be explained by the fact that digitalisation, such as the automation of previously mainly labour-intensive sectors, requires energy and also induces growth effects. In the discussion section (section 6) of this dissertation, I use the classification scheme of the three levels macro, meso and micro, as well as of direct and indirect environmental effects to classify the empirical observations into opportunities and risks, for example, with regard to the probability of rebound effects of digitalisation at the three levels. I link the investigated actor perspectives (policy makers, industry representatives), statistical data and additional literature across the system levels and consider political economy aspects to suggest fields of action for more sustainable (digitalised) industries. The dissertation thus makes two overarching contributions to the academic and societal discourse. First, my three empirical studies expand the limited state of research at the interface between digitalisation in industry and sustainability, especially by considering selected countries in the Global South and the example of China. Secondly, exploring the topic through data and methods from different disciplinary contexts and taking a socio-technical point of view, enables an analysis of (path) dependencies, uncertainties, and interactions in the socio-technical system across different system levels, which have often not been sufficiently considered in previous studies. The dissertation thus aims to create a scientifically and practically relevant knowledge basis for a value-guided, sustainability-oriented design of digitalisation in industry.
The North China Plain (NCP) is one of the most productive and intensive agricultural regions in China. High doses of mineral nitrogen (N) fertiliser, often combined with flood irrigation, are applied, resulting in N surplus, groundwater depletion and environmental pollution. The objectives of this thesis were to use the HERMES model to simulate the N cycle in winter wheat (Triticum aestivum L.)–summer maize (Zea mays L.) double crop rotations and show the performance of the HERMES model, of the new ammonia volatilisation sub-module and of the new nitrification inhibition tool in the NCP. Further objectives were to assess the models potential to save N and water on plot and county scale, as well as on short and long-term. Additionally, improved management strategies with the help of a model-based nitrogen fertiliser recommendation (NFR) and adapted irrigation, should be found.
Results showed that the HERMES model performed well under growing conditions of the NCP and was able to describe the relevant processes related to soil–plant interactions concerning N and water during a 2.5 year field experiment. No differences in grain yield between the real-time model-based NFR and the other treatments of the experiments on plot scale in Quzhou County could be found. Simulations with increasing amounts of irrigation resulted in significantly higher N leaching, higher N requirements of the NFR and reduced yields. Thus, conventional flood irrigation as currently practised by the farmers bears great uncertainties and exact irrigation amounts should be known for future simulation studies. In the best-practice scenario simulation on plot-scale, N input and N leaching, but also irrigation water could be reduced strongly within 2 years. Thus, the model-based NFR in combination with adapted irrigation had the highest potential to reduce nitrate leaching, compared to farmers practice and mineral N (Nmin)-reduced treatments. Also the calibrated and validated ammonia volatilisation sub-module of the HERMES model worked well under the climatic and soil conditions of northern China. Simple ammonia volatilisation approaches gave also satisfying results compared to process-oriented approaches. During the simulation with Ammonium sulphate Nitrate with nitrification inhibitor (ASNDMPP) ammonia volatilisation was higher than in the simulation without nitrification inhibitor, while the result for nitrate leaching was the opposite. Although nitrification worked well in the model, nitrification-born nitrous oxide emissions should be considered in future. Results of the simulated annual long-term (31 years) N losses in whole Quzhou County in Hebei Province were 296.8 kg N ha−1 under common farmers practice treatment and 101.7 kg N ha−1 under optimised treatment including NFR and automated irrigation (OPTai). Spatial differences in simulated N losses throughout Quzhou County, could only be found due to different N inputs. Simulations of an optimised treatment, could save on average more than 260 kg N ha−1a−1 from fertiliser input and 190 kg N ha−1a−1 from N losses and around 115.7 mm a−1 of water, compared to farmers practice. These long-term simulation results showed lower N and water saving potential, compared to short-term simulations and underline the necessity of long-term simulations to overcome the effect of high initial N stocks in soil.
Additionally, the OPTai worked best on clay loam soil except for a high simulated denitrification loss, while the simulations using farmers practice irrigation could not match the actual water needs resulting in yield decline, especially for winter wheat. Thus, a precise adaption of management to actual weather conditions and plant growth needs is necessary for future simulations. However, the optimised treatments did not seem to be able to maintain the soil organic matter pools, even with full crop residue input. Extra organic inputs seem to be required to maintain soil quality in the optimised treatments.
HERMES is a relatively simple model, with regard to data input requirements, to simulate the N cycle. It can offer interpretation of management options on plot, on county and regional scale for extension and research staff. Also in combination with other N and water saving methods the model promises to be a useful tool.