2/10
Firstly, this book gets an A-plus for the pun (visual and titular) that you get right from the cover.
@Rukmini is a data journalist from Chennai, one of the field's Indian pioneers and a leading voice in interpreting India's covid experience.
3/10
Her book brings together four key characteristics of a good data journalist.
First, she has a sensitivity to the importance of the process of data production. A fascinating description of how all is not what it seems in data on sexual assualt is a case in point.
4/10
As she says, there is a crime being committed against women here, just not what you think.
Second, she combines data with traditional journalism and its focus on the unique, human story. In her hands, people become numbers and numbers become people. This is a real talent.
5/10
Third, she's balanced. Not left, nor right, but willing to engage with both sides. She skewers the left with data on public hospital performance, and debates with the right on merits and weaknesses of the latest household consumption survey data that still lies unpublished
6/10
Fourth, she writes well, which is rare enough in Indian non-fiction that it is worth mentioning each time, and perhaps especially so coming from a data journalist (stereotypes aside).
So what is the picture of India that she paints? For me three big themes emerge.
7/10
First, it is an India that is profoundly conservative. Married into an English-speaking Indian family, it took me a while to understand this. We are so used to seeing English-speaking Indians thrive in the West, we overlook the very deep cultural differences.
8/10
Second, it is an India that is deeply patriarchal. From work, to home life, to politics, to health, this struck me again and again. That India ranks 10th lowest in the world for female labour force participation is perhaps the most striking statistic in the whole book.
9/10
Third, it is an India that is moving but still slower than we think, from the slow pace of urbanisation, to the slow (indeed in come cases non-existent) decoupling of "conservative" social mores with growing education, and income, and urbanisation.
10/10
As a Westerner deeply invested in India, I came away with a much stronger sense of how different and unique Indian society is. As India rises in the 21st Century, understanding this difference seems more important than ever. This book is a great place to pick up this task.
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1/n Today we published a model-based assessment of the grid integration costs of VRE.
Note: we only look at profile and balancing costs, not network costs.
Here I summarize the results in 6 easy tweets.
2/n In all scenarios we study, a short-term 'optimal' level of VRE is substantially higher than current levels, in the order of 40% of total generation.
This is robust to assumptions on demand, storage cost, cost of capital, retirement of end of life assets, etc.
3/n The substitution of expensive energy with cheap VRE allows total system costs to decline as we approach 'optimal VRE', even as total system-wide fixed costs go up.
Basic point: cheap energy + expensive capacity is a winning combination for substantially higher VRE.
1/n We ended 2020 with the news that India's power demand cross 180 GW for the first time. Unusually, this occurred in December, when power demand usually peaks is in summer?
What is going on here? Is it sign of the economic recovery?
Short thread.
2/n Firstly, as I have been repeating, we need to look carefully at both base effect and time period when looking at demand growth.
Monthly demand smooths out daily fluctuations, and comparing 2020 against both 2019 and 2018 shows the importance of the base effect. 👇
3/n Compared against 2018, 2020 monthly demand has registered only a few months of growth since the lockdown effectively ended in June.
Because of the collapse in demand in the second half of 2019, the picture looks more optimistic if we compare against 2019 (low base effect)
At 140 pages, I can't summarize the whole thing in a single thread, but I can do a series of threads.
Today's: H2 in the Indian power sector.
2/n We do a bottom up assessment to 2050 of power demand across all sectors, including direct and indirect electrification (for electrolytic H2 production).
In the low carbon scenario, power demand reaches as much as 6200 TWh by 2050, with almost 1000 TWh of that for H2. 👇
3/n This would consume a very substantial chunk of India's maximum estimated technical potential for onshore wind and solar PV. 👇
The required rate of supply growth and land footprint may be challenging!
This reinforces the message: direct electrification wherever possible.
1/n In today's thread, I want to take a look at India's NDC target of reducing the GHG intensity of GDP by 33-35% by 2030, compared to 2005.
I argue that this target is essentially BAU, because India's GHG intensity of GDP is declining as a natural part of development.
Thread
2/n If we take a long run view of the GHG intensity of India's economy since 1947, it can be seen that GHG intensity peaked in about 1985 and has been declining ever since.
Why is this?
3/n Firstly, this pattern is common to developing countries across the long-run development trajectory:
- China
- South Korea
- Thailand
- Japan
All experienced this kind of peak and decline structure (Japan and Thailand somewhat early than I graph here).
1/n Yesterday Xi Jinping announced that China would peak its emissions and work towards net zero emissions by 2060.
What does this mean for India?
A short thread on why India is fundamentally different from China, and how it could respond in its climate diplomacy.
2/n India is, simply put, a much poorer country than China. Its GDP at PPP is 57% below that of China. But I think this actually understates how far India is behind China.
Another way of looking: India's final energy consumption per capita is 70% below that of China.
3/n Even at PPP, China's final energy intensity of GDP is 30% higher than India's.
Why is this? Essentially, it boils down to economic structure 👇. China's industry share in GDP is much higher than India, and China's industry sector is more energy intensive.
1/n @KanitkarT@tjayaraman
Thank you for clarifying that the source of your claim that developed country patenting in climate mitigation technologies has collapsed from 2009-10 to 2017.
This allowed me to go back and look through the data.
A (long) thread on innovation.
2/n Firstly, as per your article, I don't think that you can use 2017 as the cut off for the analysis, because as noted in the OECD metadata "figures for the later years may be decreasing because of legal delays for publishing patent information."
3/n Taking 'priority date' as the best reference date for the patenting (described in the metadata as "closest to the invention date ... To measure inventive activity, patent should be counted according to the priority date"), then data completeness is described as follows :