Chapter 15 - National and sub-national policies and institutions

This chapter assesses national and sub-national mitigation policies and their institutional settings. There has been a marked increase in national policies and legislation on climate change since the AR4 with a diversity of approaches and a multiplicity of objectives (see Section 15.2). However, Figure 1.9 of Chapter 1 suggests that these policies, taken together, have not yet achieved a substantial deviation in emissions from the past trend. Limiting concentrations to levels that would be consistent with a likely probability of maintaining temperature increases below 2 degrees C this century (scenarios generally in the range of 430-480 ppmv CO2eq) would require that emissions break from these trends and be decreased substantially. In contrast, concentrations exceed 1000 ppmv CO2eq by 2100 in many baseline scenarios (that is, scenarios without additional efforts to reduce emissions). The literature on mitigation scenarios provides a wide range of CO2 shadow price levels consistent with these goals, with estimates of less than US$50/tCO2 in 2020 in many studies and exceeding US$100/tCO2 in others, assuming a globally-efficient and immediate effort to reduce emissions. These shadow prices exhibit a strongly increasing trend thereafter. Policies and instruments are assessed in this light. Section 15.2 assesses the role of institutions and governance. Section 15.3 lays out the classification of policy instruments and packages, while 15.4 discusses the methodologies used to evaluate policies and institutions. The performance of various policy instruments and measures are individually assessed in Sections 15.5 and 15.6. The two main types of economic instruments are price instruments, that is, taxes and subsidies (including removal of subsidies on fossil fuels), and quantity instruments - emission-trading systems. These are assessed in Sections 15.5.2 and 15.5.3 respectively. An important feature of both these instruments is that they can be applied at a very broad, economy-wide scale. This is in contrast to the regulation and information policies and voluntary agreements which are usually sector- specific. These policies are assessed in Sections 15.5.4, 15.5.5, and 15.5.7. Government provision and planning is discussed in 15.5.6. The next section, 15.6, provides a focused discussion on technology policy including research and development and the deployment and diffusion of clean energy technologies. In addition to technology policy, longer-term effects of the policies assessed in Section 15.5 are addressed in Section 15.6. Both these sections, 15.5 and 15.6, bring together lessons from policies and policy packages used at the sectoral level from Chapters 7 (Energy), 8 (Transport), 9 (Buildings), 10 (Industry), 11 (Agriculture, Forestry and Land Use) and Chapter 12 (Human Settlements, Infrastructure, and Spatial Planning). The following sections further assess the interaction among policy instruments, as they are not usually used in isolation, and the impacts of particular instruments depend on the entire package of policies and the institutional context. Section 15.7 reviews interactions, both beneficial and harmful, that may not have been planned. The presence of such interactions is in part a consequence of the multi-jurisdictional nature of climate governance as well as the use of multiple policy instruments within a jurisdiction. Section 15.8 examines the deliberate linkage of policies across national and sub-national jurisdictions. Other key issues are further discussed in dedicated sections. They are: the role of stakeholders including non-governmental organizations (NGOs) (15.9), capacity building (15.10), links between adaptation and mitigation policies (15.11), and investment and finance (15.12). Gaps in knowledge are collected in 15.13

Governing policy evaluation? Towards a new typology

As policy evaluation matures, thoughts are turning to its governance. However, few scholars have combined insights from the evaluation and governance literatures to shed new light on this matter. In order to address this important gap, this article develops a new typology of ways to comprehend and perhaps ultimately govern ex-post policy evaluation activities. The article then explores its validity in the context of climate policy evaluation activities, a vibrant policy area in which the demand for and practices of evaluation have grown fast, particularly in Europe. The analysis reveals that the typology usefully guides new thinking, but also highlights important gaps in our empirical knowledge of the various modes of governing policy evaluation. The article identifies a need for a new research agenda that simultaneously develops a fuller understanding of these evaluation practices and the options for governing them

Performance Measurement of Small Cell Power Management Mechanism in 5G Cellular Networks using Firefly Algorithm

In cellular networks, with the increase in demand, designing a base station (BS) with less energy consumption remains a challenge for researchers. Also, in a heterogeneous network that is dense in nature, the distribution of numerous small BS has become a challenging issue in terms of expanding the cost of energy. In this paper, we investigate an optimized nature-based cluster sleep technique for reducing the power consumption in the BS as well as the interference in the network. The small BS are grouped along with the interference, which is assumed to be the cluster, which is quite large, where the fire fly (FF) algorithm is applied to frame the sleep technique for the small BS. These FF algorithms, which are based on fire fly attractiveness behavior, improve connectivity among the base stations in an energy-efficient way. The outcomes reveal that the projected sleep technique with the FF algorithm reduces the power consumed by the BS and also gives satisfactory performance for mobile users. The results were compared with the other techniques, such as BS conventional sleep mode and BS sleep mode with LEACH. The proposed method outperformed the other techniques

Attitudes of sperm, egg and embryo donors and recipients towards genetic information and screening of donors

© 2018 The Author(s). Background: Gamete and embryo donors undergo genetic screening procedures in order to maximise the health of donor-conceived offspring. In the era of genomic medicine, expanded genetic screening may be offered to donors for the purpose of avoiding transmission of harmful genetic mutations. The objective of this study was to explore the attitudes of donors and recipients toward the expanded genetic screening of donors. Methods: Qualitative interview study with thematic analysis, undertaken in a tertiary fertility centre. Semi-structured in-depth qualitative interviews were conducted with eleven recipients and nine donors from three different cohorts (sperm, egg and embryo donors/recipients). Results: Donors and recipients acknowledged the importance of genetic information and were comfortable with the existing level of genetic screening of donors. Recipients recognised some potential benefits of expanded genetic screening of donors; however both recipients and donors were apprehensive about extended genomic technologies, with concerns about how this information would be used and the ethics of genetic selectivity. Conclusion: Participants in donor programs support some level of genetic screening of donors, but are wary of expanding genetic screening beyond current levels

Technical summary

The Working Group III (WGIII) contribution to the IPCC's Fifth Assessment Report (AR5) assesses literature on the scientific, technological, environmental, economic and social aspects of mitigation of climate change. It builds upon the WGIII contribution to the IPCC's Fourth Assessment Report (AR4), the Special Report on Renewable Energy Sources and Climate Change Mitigation (SRREN) and previous reports and incorporates subsequent new findings and research. Throughout, the focus is on the implications of its findings for policy, without being prescriptive about the particular policies that governments and other important participants in the policy process should adopt. In light of the IPCC's mandate, authors in WGIII were guided by several principles when assembling this assessment: (1) to be explicit about mitigation options, (2) to be explicit about their costs and about their risks and opportunities vis-a-vis other development priorities, (3) and to be explicit about the underlying criteria, concepts, and methods for evaluating alternative policies. This summary offers the main findings of the report

Archimedes assisted LSTM model for blockchain based privacy preserving IoT with smart cities

Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long short-term memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM’s hyper-parameters. In addition, the BC technology is utilized for securing the data transmission

MobileNet based secured compliance through open web application security projects in cloud system

The daunting issues that are promptly faced worldwide are the sophisticated cyber-attacks in all kinds of organizations and applications. The development of cloud computing pushed organizations to shift their business towards the virtual machines of the cloud. Nonetheless, the lack of security throughout the programmatic and declarative levels explicitly prone to cyber-attacks in the cloud platform. The exploitation of web pages and the cloud is due to the uncrated open web application security projects (OWASP) fragilities and fragilities in the cloud containers and network resources. With the utilization of advanced hacking vectors, the attackers attack data integrity, confidentiality, and availability. Hence, it’s ineluctable to frame the application security-based technique for the reduction of attacks. In concern to this, we propose a novel Deep learning-based secured advanced web application firewall to overcome the lack of missing programmatic and declarative level securities in the application. For this, we adopted the MobileNet-based technique to ensure the assurance of security. Simulations are effectuated and analyzed the robustness with the statistical parameters such as accuracy, precision, sensitivity, and specificity and made the comparative study with the existing works. Our proposed technique surpasses all the other techniques and provides better security in the cloud

Detection of cyberattacks using bidirectional generative adversarial network

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively

Automated adversarial detection in mobile apps using API calls and permissions

Android mobile phones’ growing popularity has led to developers creating more malicious apps, which can be included in third-party arcades as protected applications. Detecting these malware applications is challenging due to time-consuming and high-cost techniques. This study proposes a robust deep learning (DL) model for detecting adversarial third-party apps using adaptive feature learning. The strategy involves preprocessing raw apk files, extracting permission behavioral features, and using the proposed spatial dropout-assisted convolutional autoencoder (SD_ConvAE) model to determine if the app is benign or malignant. The approach is simulated using a Python tool and assessed using various measures like accuracy, recall, weighted F-score (W-FS), false discovery rate (FDR), and kappa coefficient. The overall accuracies achieved by the developed techniques are about 99.6% and 99% for detecting benign and malignant apps, respectively