The proposed research aspires to provide new insight on issues of applied Roadmapping and advance the state of the art in Roadmapping and its practice. It provides a conceptual model and an integrated process framework for the development of a Third Generation, Meso-level, Co-innovation Dynamic Roadmapping (from now on called ‘Dynamic Roadmapping’), which integrates policy, research, industry, and organisational roadmapping methodologies, in order to manage the development and adoption of systemic innovations in complex domains. It has been developed to meet the needs of increasingly complex systemic innovations where multiple organisations are involved as co-innovators and many other intermediaries and decision makers need to be included in the innovation adoption process. These types of innovations are usually driven by the interplay of multi-dimensional and cross-impacting factors derived from changes in social, market, economic, political and technology systems. Thus, the ‘Dynamic Roadmapping’ does not presuppose a single desired future for complex domains, but several futures, based on the complementary strategic perspectives of inter-dependent stakeholders, which need to be contextualised and negotiated at various sectoral, national and regional levels in order to be adopted. The ‘Dynamic Roadmapping’ approach supports the achievement of the realisation of the desired futures through two main components: a ‘co-innovation group’ and an ‘observatory function’. The co-innovation group is formed from all the necessary co-innovators, adopters, decision makers and users that are needed in order for the innovations to be developed and adopted. Their function is predominately ‘normative’ describing “what they want to happen” and “how” it will happen. The observatory function provides foresight and sense making methodologies to the co-innovation group, in order to constantly review and adapt their roadmaps in light of the emerging changes that can impact the roadmaps’ realisation and adoption. A conceptual model and its theoretical grounding have been built in order to bridge support for roadmapping activities among different innovative communities (e.g. in policy, research, industry and practice) and foster their collaboration via stakeholders’ innovation networks. The proposed conceptual model and its process framework have been evaluated in a case study in order to establish its validity in the European context and provide implications to theory and practice. A pilot of this framework is first implemented for the area of Technology Enhanced Learning (TEL). The impact of this research is: - Managing uncertainty in Future planning - Managing and implementing emergent Roadmaps for systemic innovations - Monitoring and adapt the produced Roadmaps according to change factors in emerging reality - Ensure their adoption in complex domain This research work has been funded by an EU Marie-Curry Fellowship grant via the DYRECT project no. 255182. The proposed integrated framework has been adopted by the EU TEL-Map project (in education sector) and EU CRe-AM project (in creative industry sector). It has been documented in many European project deliverables as well as in international conference papers, and in journal papers.

In recent years, numerous high tech companies have developed and used technology roadmaps when making their investment decisions. Jay Paap has proposed the Customer Focused Technology Planning (CFTP) framework to draw future technology roadmaps. However, the CFTP framework does not include risk assessment as a critical factor in decision making. The problem addressed in this quantitative study was that high tech companies are either losing money or getting a much smaller than expected return on investment when making technology investment decisions. The purpose of this research was to determine the relationship between returns on investment before and after adding risk assessment to the CFTP framework. Paap's CFTP framework and process to improve technology investments thus served as the theoretical framework for this study. Data were obtained from cloud computing companies using the companies' market risk data and actual returns on investment data. The results and findings of paired sample two-tailed t tests for means and equal variances showed that return on investment was positively related to adding a traditional risk assessment model to Paap's CFTP framework. These findings regarding the addition of risk assessment to the technology investment framework may be used by investors to (a) make better and more expeditious decisions, and (b) obtain a high return on technology investment by selecting the highest return value and lowest risk value.

Dans le contexte du réchauffement climatique global, les institutions académiques et internationales comme GIEC et de nombreux pays ont proposé des objectifs de réduction des émissions de CO2. L'objectif de cette thèse est d'évaluer ces objectifs gouvernementaux en les comparants avec les objectifs globaux à l'aide de différentes méthodes d'allocations lesquelles correspondent à différents principes d'équité en matière d'émissions carbones.Afin d'évaluer les feuilles de route technologique permettant d'obtenir les réductions d'émission de CO2 nécessaires, un modèle qualifié de flexible est proposé à destination des décideurs. Notre modèle permet d'éviter les opérations informatiques complexes et peut être personnalisé en fonction de différents besoins. Les simulations sont réalisées jusqu'à l'horizon 2050 lequel est souvent considéré comme un pivot dans les habitudes de consommation d'énergies notamment. Dans cette thèse, des feuilles de route technologique pour les différents objectifs gouvernementaux en matières d'émissions de CO2 sont étudiées pour trois pays : la Chine, la France et les États-Unis. Le modèle couvre les principaux secteurs responsables des émissions de CO2 et étudie l'influence de différentes technologies sur le mix énergétique. Diverses méthodes et approches sont utilisées dans notre modélisation. L'identité IPAT est utilisée pour la décomposition des émissions dans les secteurs de l'énergie. Le modèle STIRPAT permet quant à lui d'évaluer l'évolution des émissions de CO2 dans les scénarios Business-as-Usual. Le modèle SVR est utilisé dans le cadre des projections de production d'électricité. Enfin, l'indice de Theil est employé pour mesurer les inégalités d'émissions de CO2 par tête. A la différence des modèles plus classiques en économie de l'énergie, notre modèle propose des feuilles de route technologiques selon différents critères, comme par exemple avec le développement « équilibré » de la technologie entre les secteurs, ou le critère de disponibilité des ressources énergétiques. Par ailleurs, l'équité carbone, avec la convergence des technologies dans les secteurs à long terme, peut être mise en œuvre dans notre modèle et joue, dans ce cas, comme une contrainte supplémentaire dans l'optimisation multi-objectifs.Nos résultats montrent que les objectifs gouvernementaux en France et aux États-Unis sont « très stricts » car, pour les atteindre, tous les secteurs doivent réaliser des efforts importants de réduction de CO2. En revanche, l'objectif gouvernemental de la Chine s'avère « plus facile » à réaliser car les progrès dans les technologies qui sont nécessaires sont moins exigeants.Plus précisément, si on prévoit que le mix énergétique reste inchangé en Chine et aux États-Unis, le CSC deviendra indispensable dans le secteur de l'énergie. Pour la France, 80% des voitures devront être remplacées par des véhicules électriques afin d'atteindre son objectif en matière de CO2.Toutefois, en considérant l'équité carbone entre secteurs, la combustion du charbon est censée être réduite de deux tiers en Chine et devra être pratiquement éliminée aux États-Unis. Par contre, le gaz peut être encouragé dans son utilisation dans le secteur de l'énergie en particulier aux États-Unis. Concernant le secteur du transport, plus de 60 % des véhicules doivent être remplacés par des véhicules électriques en Chine. Cette part serait d'environ 90 % en France et aux États-Unis.Enfin, la sensibilité des paramètres du modèle a été testée pour simulations, à chaque étape du travail, et pour toutes les roadmaps technologiques. Les résultats des tests de sensibilité montrent que la production d'électricité et l'intensité d'émissions sont les deux paramètres dont l'influence est la plus importante sur les émissions futures de CO2. Ainsi l'amélioration de l'efficacité de la combustion du charbon et de l'efficacité énergétique de l'électricité joueront un rôle central dans la réductions des émissions de CO2. / In the context of global warming, academic institutes, international institutions such as the IPCC, and governments of numerous countries have proposed global objectives of reducing CO2 emissions and announced national targets. The purpose of this thesis is to assess the governmental targets in comparing with the global objectives of various allocation methods, which correspond to different carbon equity principles.In order to evaluate the technology roadmaps which are necessary to achieve these reductions of CO2 emissions, a flexible modeling framework is proposed for policy makers. Our sectoral model avoids the complex computing operations. It can be customized according to different requirements and situations. We simulate the model up to the horizon 2050, which is often seen as a turning point in energy use patterns worldwide – forced by the probable decline in hydrocarbons extraction.In the thesis, the technology roadmaps for the governmental targets on CO2 emissions are studied for three typical countries: China, France, and the United States. The model covers the sectors responsible for the greatest part of CO2 emissions: power, transport, residence and industry sector, in studying the impacts of the principle energy technologies, such as energy mix, Carbon Capture and Storage (CCS), electric vehicles and energy efficiency.Various methods and approaches are used in our modeling. IPAT identity - which assumes the environment Impact is the results of Population, Affluence and Technology - is employed in the power sector emission decomposition. Besides STIRPAT - for Stochastic Impacts by Regression on Population, Affluence and Technology - model is used for the projection of CO2 emissions in the Business-as-Usual scenario. Then SVR - for Support Vector Regression - is used to forecast electricity production. Finally, the Theil index is employed as the measurement of per capita CO2 emission inequality. Different from classic cost-effective energy system models, our model provides the technology pathways for different criteria, such as balanced development of energy technology across sectors, availability of energy resources, etc. Besides, the carbon equity is employed as one of the constraints in the multi-objective optimization, under the consideration of the convergence of technologies in sectors in the long-term.Our results show that the governmental targets in France and the United States prove very strict, as they require all sectors to make large efforts in reducing CO2 emissions. In contrast, the governmental target in China seems more easily achievable, as the necessary advances of technologies are less demanding. More precisely: if the energy mix is expected to be kept unchanged in China and in the United States of America, the CCS prove indispensable in the power sector. In France, 80% of automobiles are required to be changed into electric vehicles, in order to get the target of CO2 emissions.However, under the sectoral carbon equity consideration, coal combustion is projected to be reduced by two thirds in China, and it will have to be almost eliminated in the United States to achieve their CO2 reduction target. But gas is encouraged to be used in the power sector, especially in the United States. Regarding the transport sector, more than 60% of vehicles should be replaced to electric vehicles in China, and this share will be about up to 90% in France and the United States.Finally the sensitivity of parameters in the model is tested for a robust simulation, at each step of the work, and for all technology roadmaps. The results of the sensitivity tests show that electricity production and the emission intensity of production are the two parameters with the most important influence on CO2 emissions. Thus improving the efficiency of coal combustion and the energy efficiency of electricity will play an important role in the CO2 emission reductions.