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Although India is seen as a key player in the global economy, it is still below its potential level of growth. In this age of globalism, integration with the global economy through trade and foreign investments fosters domestic growth. For India, although this integration has strengthened over the years, there are certain gaps that remain to be addressed. Though numerous studies in the literature have tried to find answers to these questions, an important aspect that has not been considered by these studies relates to India’s federal structure and the role of states in determining the aggregate economic outcome. As Foreign Direct Investment (FDI) inflows to India are concentrated in a few states, this paper aims to provide an assessment of the reasons behind this trend.

This paper aims to investigate the reasons behind the interstate differences with respect to FDI inflows in India. The analytical work undertaken for this paper is based on secondary data, collected and collated from various sources. The approach adopted for this paper includes a heat graph analysis to examine whether there is a clear pattern in terms of the state-specific factors for high FDI states versus the low FDI states. This data analysis is followed by an econometric estimation to gauge the impact of state-specific factors in determining the FDI inflows.

As per the secondary data–driven heat graph and econometric analysis, factors like industrial output, social sector expenditure, judicial quality, connectivity indicators, labor cost and availability of credit, act as differentiators between high and low FDI-receiving states. It then becomes imperative to bridge the gap between the two sets of states in terms of these specific factors. Implementation and success of policy interventions can only be derived at the state level and therefore needs more decentralized approach.

This paper tries to identify the reasons that are responsible for FDI inflows being concentrated in a few Indian states. This involves a comprehensive analysis of several variables to understand whether there is a clear pattern where high-FDI states are also in a better position with respect to these attributes. This effort to factor in the federal aspect of a macroeconomic indicator like FDI provides new dynamic to this area of work.

Following the trend in the world over and on the African continent, the Parliament of Ghana passed a new law in 2020, the Narcotics Control Commission Act (Act 1019) that eased the legal restrictions on cannabis cultivation and use, subject to obtaining license from the relevant authority/authorities. This paper aims to examine the implications of Ghana’s decision to legalise the cultivation of cannabis for industrial purposes for the production of fibre and for medicinal purposes.

The author adopted the qualitative analysis critical review methodology in sourcing information from peer-reviewed articles, coupled with the author’s own professional knowledge, background and training, to critically analyse and review the reasons behind Ghana’s decision to legalise cannabis cultivation and the implications such a major policy change/shift.

It was found that, while the law mentions industrial and medicinal purposes for the legalisation of cannabis, the real reason, at least in the immediate term, is mainly economic. This agrees with those of other African countries that have legalised cannabis cultivation but appears to contrast with those of Europe and America.

This research is limited to Ghana and the few African countries that have legalised cannabis cultivation so far. The main reason for the legalisation (economic benefits) may also change in the future when the economics of scale turn in the favour of the country. Therefore, Ghana’s case may not represent the case of all countries legalising cannabis cultivation.

The research implication of this paper is that it brings to the world the law on cannabis cultivation in Ghana. It provides a professional and scientific position paper on Ghana’s drug law regime regarding cannabis, thus, serving as a useful reference literature to the academic and research community on the topic. More importantly, it discusses the implications of such major policy change from an objective perspective, unravelling the real reason behind the State’s decision to legalise the cultivation of cannabis in Ghana.

This paper will bring to the fore the other consequences of Ghana’s cannabis cultivation legalisation policy change, which little attention has been devoted to so far.

This paper, apart from reference to other published texts on the topic, which have been duly acknowledged, represents the sole work of the author. It has not been produced anywhere or by anyone else either in Ghana or elsewhere.

Initiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.

We compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.

By analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.

Our findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.