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The ambitious climate targets set by industrialized nations worldwide cannot be met without decarbonizing the building stock.
Using Germany as a case study, this paper takes stock of the extensive set of energy efficiency policies that are already in place and clarifies that they have been designed "in good faith" but lack in overall effectiveness as well as cost-efficiency in achieving these climate targets.
We map out the market failures and behavioural considerations that are potential reasons for why realized energy savings fall below expectations and why the household adoption of energy-efficient and low-carbon technologies has remained low. We highlight the pressing need for data and modern empirical research to develop targeted and cost-effective policies seeking to correct these market failures.
To this end, we identify some key research questions and identify gaps in the data required for evidence-based policy.
Beyond good faith
(2021)
The ambitious climate targets set by industrialized nations worldwide cannot be met without decarbonizing the building stock. Using Germany as a case study, this paper takes stock of the extensive set of energy efficiency policies that are already in place and clarifies that they have been designed “in good faith” but lack in overall effectiveness as well as cost-efficiency in achieving these climate targets. We map out the market failures and behavioural considerations that are potential reasons for why realized energy savings fall below expectations and why the household adoption of energy-efficient and low-carbon technologies has remained low. We highlight the pressing need for data and modern empirical research to develop targeted and cost-effective policies seeking to correct these market failures. To this end, we identify some key research questions and identify gaps in the data required for evidence-based policy.
Friends or foes?
(2020)
Energy efficiency measures and the deployment of renewable energy are commonly presented as two sides of the same coin-as necessary and synergistic measures to decarbonize energy systems and reach the temperature goals of the Paris Agreement. Here, we quantitatively investigate the policies and performances of the EU Member States to see whether renewables and energy efficiency policies are politically synergistic or if they rather compete for political attention and resources. We find that Member States, especially the ones perceived as climate leaders, tend to prioritize renewables over energy efficiency in target setting. Further, almost every country performs well in either renewable energy or energy efficiency, but rarely performs well in both. We find no support for the assertion that the policies are synergistic, but some evidence that they compete. However, multi-linear regression models for performance show that performance, especially in energy efficiency, is also strongly associated with general economic growth cycles, and not only efficiency policy as such. We conclude that renewable energy and energy efficiency are not synergistic policies, and that there is some competition between them.
The sustainability of agro-bioenergy systems is dependent on many factors, some local or regional in implementation, some others global in nature. This study assessed the effects of often ignored local and regional factors (e.g. alternative agronomic factor options, alternative agricultural production systems, alternative biomass flows, alternative conversion technologies etc. The results from this study suggests that key to enhancing the energy efficiency (and by extension the sustainability) of agro-bioenergy systems is paying attention to local and regional factors such as biomass conversion technology, alternative agronomic factor options, alternative agricultural production systems and available biomass flows.
In light of possible future restrictions on the use of fossil fuel, due to climate change obligations and continuous depletion of global fossil fuel reserves, the search for alternative renewable energy sources is expected to be an issue of great concern for policy stakeholders. This study assessed the feasibility of bioenergy production under relatively low-intensity conservative, eco-agricultural settings (as opposed to those produced under high-intensity, fossil fuel based industrialized agriculture). Estimates of the net energy gain (NEG) and the energy return on energy invested (EROEI) obtained from a life cycle inventory of the energy inputs and outputs involved reveal that the energy efficiency of bioenergy produced in low-intensity eco-agricultural systems could be as much as much as 448.5–488.3 GJ·ha−1 of NEG and an EROEI of 5.4–5.9 for maize ethanol production systems, and as much as 155.0–283.9 GJ·ha−1 of NEG and an EROEI of 14.7–22.4 for maize biogas production systems. This is substantially higher than for industrialized agriculture with a NEG of 2.8–52.5 GJ·ha−1 and an EROEI of 1.2–1.7 for maize ethanol production systems, as well as a NEG of 59.3–188.7 GJ·ha−1 and an EROEI of 2.2–10.2 for maize biogas production systems. Bioenergy produced in low-intensity eco-agricultural systems could therefore be an important source of energy with immense net benefits for local and regional end-users, provided a more efficient use of the co-products is ensured.
Virtualized cloud data centers provide on-demand resources, enable agile resource provisioning, and host heterogeneous applications with different resource requirements. These data centers consume enormous amounts of energy, increasing operational expenses, inducing high thermal inside data centers, and raising carbon dioxide emissions. The increase in energy consumption can result from ineffective resource management that causes inefficient resource utilization. This dissertation presents detailed models and novel techniques and algorithms for virtual resource management in cloud data centers. The proposed techniques take into account Service Level Agreements (SLAs) and workload heterogeneity in terms of memory access demand and communication patterns of web applications and High Performance Computing (HPC) applications. To evaluate our proposed techniques, we use simulation and real workload traces of web applications and HPC applications and compare our techniques against the other recently proposed techniques using several performance metrics. The major contributions of this dissertation are the following: proactive resource provisioning technique based on robust optimization to increase the hosts' availability for hosting new VMs while minimizing the idle energy consumption. Additionally, this technique mitigates undesirable changes in the power state of the hosts by which the hosts' reliability can be enhanced in avoiding failure during a power state change. The proposed technique exploits the range-based prediction algorithm for implementing robust optimization, taking into consideration the uncertainty of demand. An adaptive range-based prediction for predicting workload with high fluctuations in the short-term. The range prediction is implemented in two ways: standard deviation and median absolute deviation. The range is changed based on an adaptive confidence window to cope with the workload fluctuations. A robust VM consolidation for efficient energy and performance management to achieve equilibrium between energy and performance trade-offs. Our technique reduces the number of VM migrations compared to recently proposed techniques. This also contributes to a reduction in energy consumption by the network infrastructure. Additionally, our technique reduces SLA violations and the number of power state changes. A generic model for the network of a data center to simulate the communication delay and its impact on VM performance, as well as network energy consumption. In addition, a generic model for a memory-bus of a server, including latency and energy consumption models for different memory frequencies. This allows simulating the memory delay and its influence on VM performance, as well as memory energy consumption. Communication-aware and energy-efficient consolidation for parallel applications to enable the dynamic discovery of communication patterns and reschedule VMs using migration based on the determined communication patterns. A novel dynamic pattern discovery technique is implemented, based on signal processing of network utilization of VMs instead of using the information from the hosts' virtual switches or initiation from VMs. The result shows that our proposed approach reduces the network's average utilization, achieves energy savings due to reducing the number of active switches, and provides better VM performance compared to CPU-based placement. Memory-aware VM consolidation for independent VMs, which exploits the diversity of VMs' memory access to balance memory-bus utilization of hosts. The proposed technique, Memory-bus Load Balancing (MLB), reactively redistributes VMs according to their utilization of a memory-bus using VM migration to improve the performance of the overall system. Furthermore, Dynamic Voltage and Frequency Scaling (DVFS) of the memory and the proposed MLB technique are combined to achieve better energy savings.