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The Indian pharmaceutical industry currently faces multiple challenges, including rising costs and slowing export growth, which in turn have limited its ability to expand presence in global canvas. Given the nature of sectoral dynamics, a pharmaceutical firm must undertake huge investments in R&D to introduce product innovation, in turn enhancing market share and sustaining profit streams. The development of novel medicines, confirmed by the granting of patent rights, provides a pharma company edge over its competitors. In addition, presence of innovator firms within the industry invigorates the sectoral value chain and raises efficiency. Hence, it is important to analyze whether granting patent rights enhances the exports of pharmaceutical products in the Indian context.

The current study explored this question using a simultaneous-equation framework. Specifically, the authors use the methods developed by Davidson and MacKinnon (1993) and Greene (2003) to obtain heteroscedasticity-consistent estimates. The time-series properties of the data were further probed, and robust estimates were used to test the theory. Methods developed by Baltagi (1981) have been used further to refine the authors’ estimations.

After controlling for relevant variables, it is observed that granting of patents caused a significantly positive impact on pharmaceutical exports. Furthermore, the change in the patent administration regime had a significant impact on patent fillings, which further impacted their exportability. Compared to patents granted patents filed had a higher impact on pharmaceutical exports.

This study attempts to apply the framework developed by Goldstein and Khan (1978) with necessary modifications to suit the context of a developing country. The application of the 3SLS method to estimate the export supply equation for pharmaceutical products is a novel approach to the research question in general and to the Indian context in particular. System autocorrelation and heteroscedasticity tests were performed to refine the results further.

The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.