• Energy Research
• 12. Responsible consumption close
• 3. Good health close
• 4. Education close
• ZENODO close

This work models a last-mile network design problem for an e-retailer with a capacitated two-echelon distribution structure - typical in e-retail last-mile distribution, catering to a market with a stochastic and dynamic daily customer demand requesting delivery within time-windows. Considering the distribution evnironment, this work formulates last-mile network design problem for this e-retailer as a dynamic-stochastic two capacitated location routing problem with time-windows. In doing so, this work splits the last-mile network design problem into its constituent strategic, tactical, and operational decisions. Here, the strategic decisions undertake long-term planning to develop a distribution structure with appropriate distribution facilities and a suitable delivery fleet to service the expected customer demand in the planning horizon. The tactical decisions pertain to medium-term day-to-day planning of last-mile delivery operations to establish efficient goods flow in this distribution structure to service the daily stochastic customer demand. And finally, operational decisions involve immediate short-term planning to fine-tune this last-mile delivery to service the requests arriving dynamically through the day. Note, the last-mile network design problem formulated as a location routing problem constitutes three subproblems encompassing facility location problem, customer allocation problem, and vehicle routing problem, each of which are NP-hard combinatorial optimization problems. To this end, this work develops an adaptive large neighborhood search meta-heuristic algorithm that searches through the neighborhood by destroying and consequently repairing the solution thereby reconfiguring large portions of the solution with specific operators that are chosen adaptively in each iteration of the algorithm, hence the name adaptive large neighborhood search. Further, considering the stochastic and dynamic nature of the delivery environment, this work develops a Monte-Carlo framework simulating each day in the planning horizon, with each day divided into 1-hr timeslots, and with each time-slot accepting customer requests for service by the end of the day. In particular, the framework assumes the e-retailer will delay route commitments until the last-feasible time-slot to accumulate customer requests and consequently assign them to an uncommitted delivery route. Note, a delivery route is committed once the e-retailer starts loading packages assigned to this delivery route onto the delivery vehicle assigned for this delivery route. At the end of every time-slot then, this framework assumes the e-retailer integrates the new customer requests by inserting these customer nodes into such uncommitted delivery routes in a manner that results in the least increase in distribution cost keeping the customer-distribution facility allocation fixed. Thus, the framework iterates through the time-slots with the e-retailer processing route commitments, accumulating customer requests, and subsequently integrating them into the delivery operations for the day. E-commerce has the potential to make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with such alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2024-07-05. Changes in this version compared to version 2024-07-04: No data changes. Provide the full dataset as a single parquet file instead of a collection of parquet files in a zip file. The dataset is also available via datalad. To obtain the dataset with datalad, see the instructions at https://github.com/mikapfl/unfccc_di_data .

Cities are progressively heightening their climate aspirations to curtail urban carbon emis- sions and establish a future where economies and communities can flourish within the Earth’s eco- logical limits. Consequently, numerous climate initiatives are being launched to control urban car- bon emissions, targeting various sectors, including transport, residential, agricultural, and energy. However, recent scientific literature underscores the disproportionate distribution of climate poli- cies. While cities in the Global North have witnessed several initiatives to combat climate change, cities in the Global South remain uncovered and highly vulnerable to climate hazards. To address this disparity, we employed the Balanced Iterative Reducing and Clustering using the Hierarchies (BRICH) algorithm to cluster cities from diverse geographical areas that exhibit comparable socio- economic profiles. This clustering strives to foster enhanced cooperation and collaboration among cities globally, with the goal of addressing climate change in a comprehensive manner. In summary, we identified similarities, pa􏰀erns, and clusters among peer cities, enabling mutual and generaliza- ble learning among worldwide peer-cities regarding urban climate policy exchange. This exchange occurs through three approaches: (i) inner-mutual learning, (ii) cross-mutual learning, and (iii) outer-mutual learning. Our findings mark a pivotal stride towards a􏰀aining worldwide climate ob- jectives through a shared responsibility approach. Furthermore, they provide preliminary insights into the implementation of “urban climate policy exchange” among peer cities on a global scale.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

DataForAssessingSeasonalEffectsOnElectricityAndWaterForRepositoryThis Excel file contains data used to conduct a seasonal analysis to assess whether seasonal patterns exist in electricity use in dorms and whether these patterns differ by latitude. The first worksheet contains metadata.Fall 2010 Campus Conservation Nationals surveyThis online survey was administered to students attending colleges who participated in the Fall 2010 Campus Conservation Nationals competition. Not all schools who participated in the competition administered the survey.CCN_F10_survey.pdfSpring 2012 Campus Conservation Nationals surveyThis online survey was administered to students attending colleges who participated in the Spring 2012 Campus Conservation Nationals competition. Not all schools who participated in the competition administered the survey.CCN_Spring12_survey.pdfFall 10 Campus Conservation Nationals electricity, water, webhit, and commitment dataThis data file contains data at the dorm level collected by Lucid before, during, and after the Fall 2010 CCN competition. The first sheet contains metadata defining all variable names.Fall10_CCN_elec_water_webhits_commitments.xlsxSpring 2012 Campus Conservation Nationals electricity, water, and commitment dataThis data file contains data at the dorm level collected by Lucid before, during, and after the Spring 2012 CCN competition. The first sheet contains metadata defining all variable names.Spring12_CCN_elec_water_commitments_no.xlsxFall 10 CCN data aggregated at dorm level with psychological variablesThis data file contains data at the dorm level collected from our online survey and merged with the resource use data. The first sheet contains metadata defining all variable names.Fall10_CCN_dormagg_with_psych_variables.xlsxSpring 2012 CCN data with psychological variablesThis data file contains data at the dorm level collected from our online survey and merged with the resource use data. The first sheet contains metadata defining all variable names.Spring12__CCN_dormagg_with_psych_variables.xlsx “Campus Conservation Nationals” (CCN) is a recurring, nation-wide electricity and water-use reduction competition among dormitories on college campuses. We conducted a two year empirical study of the competition’s effects on resource consumption and the relationship between conservation, use of web technology and various psychological measures. Significant reductions in electricity and water use occurred during the two CCN competitions examined (n = 105,000 and 197,000 participating dorm residents respectively). In 2010, overall reductions during the competition were 4% for electricity and 6% for water. The top 10% of dorms achieved 28% and 36% reductions in electricity and water respectively. Participation was larger in 2012 and reductions were slightly smaller (i.e. 3% electricity). The fact that no seasonal pattern in electricity use was evident during non-competition periods suggests that results are attributable to the competition. Post competition resource use data collected in 2012 indicates that conservation behavior was sustained beyond the competition. Surveys were used to assess psychological and behavioral responses (n = 2,900 and 2,600 in 2010 and 2012 respectively). Electricity reductions were significantly correlated with: web visitation, specific conservation behaviors, awareness of the competition, motivation and sense of empowerment. However, participants were significantly more motivated than empowered. Perceived benefits of conservation were skewed towards global and future concerns while perceived barriers tended to be local. Results also suggest that competitions may be useful for “preaching beyond the choir” – engaging those who might lack prior intrinsic or political motivation. Although college life is distinct, certain conclusions related to competitions, self-efficacy, and motivation and social norms likely extend to other residential settings.

QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes: Socially feasible scaling of energy technologies Policy preferences & dynamics Barriers to infrastructural development (wind energy, grid development) Citizen energy Private energy demand Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4: S��sser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox���Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS). S��sser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).

Indicators of economic sustainability obtained for the 8 systems of LTS studied. Monoc. = monoculture; sub-trop. = subtropical; IMTA = integrated multi trophic aquaculture; “-“ = no data.

Batteries have the potential to significantly reduce greenhouse gas emissions from on-road transportation. However, environmental and social impacts of producing lithium-ion batteries, particularly cathode materials, and concerns over material criticality are frequently highlighted as barriers to widespread electric vehicle adoption. Circular economy strategies, like reuse and recycling, can reduce impacts and secure regional supplies. To understand the potential for circularity, we undertake a dynamic global material flow analysis of pack-level materials that includes scenario analysis for changing battery cathode chemistries and electric vehicle demand. Results are produced regionwise and through the year 2040 to estimate the potential global and regional circularity of lithium, cobalt, nickel, manganese, iron, aluminum, copper, and graphite, although the analysis is focused on the cathode materials. Under idealized conditions, retired batteries could supply 60% of cobalt, 53% of lithium, 57% of manganese, and 53% of nickel globally in 2040. If the current mix of cathode chemistries evolves to a market dominated by NMC 811, a low cobalt chemistry, there is potential for 85% global circularity of cobalt in 2040. If the market steers away from cathodes containing cobalt, to an LFP-dominated market, cobalt, manganese, and nickel become less relevant and reach circularity before 2040. For each market to benefit from the recovery of secondary materials, recycling and manufacturing infrastructure must be developed in each region. This data was collected through various sources, including from EV Volumes, International Energy Agency, Argonne National Lab, and published articles. A model was created with R to process the data.  R is required to open the models.